Real-time Object Identification Research Articles

Real-Time Fire Surveillance with Machine Learning Twilio Integration

Fire threats pose significant risks to people and property, necessitating efficient surveillance systems. A complete fire surveillance system is proposed using machine learning and artificial intelligence to monitor fire incidents in real time. The system is built using React.js for frontend development and MongoDB for backend storage. Node.js is integrated for server-side operations, ensuring data management and user interaction. The system sends alerts via WhatsApp when image analysis identifies a fire concern, leveraging Twilio for seamless messaging. Robo Flow simplifies computer vision model management, while YOLOv8, a cutting-edge object recognition algorithm, enhances detection speed and accuracy. YOLOv8 is widely used in real-time object identification applications like robotics, autonomous cars and surveillance systems. Twilio is a cloud communication platform that allows developers to integrate voice, video, and SMS into their apps, enabling notifications, alarms, and two-way communication. The research utilizes Machine Learning, React.js, MongoDB, YOLOv8, and Twilio to offer efficient real-time fire surveillance.

Audio Enabled Object Detection System with NLP based Interaction

The project aims to develop an innovative object detection system that uses audio input and output to enhance user engagement and provide detailed product information. The system employs deep learning techniques for real-time object identification, generating an audio-based output with the object's name. In order to enable users to interact with the system verbally, it also incorporates Natural Language Processing (NLP), which interprets and processes speech in order to identify the object. The system also includes an online search module, which provides users with descriptions of the object's attributes and potential applications. The system undergoes rigorous testing and optimization to ensure accuracy and responsiveness. The project employs user-friendly technology that can assist people with low vision or disabilities, providing a practical and time-saving alternative for seeking instant information about objects. In order to make object recognition and information retrieval a smooth and inclusive experience for all users, this audio-enabled system is a first step towards bridging the gap between artificial intelligence and human-technology interaction. Key Words: Object Detection, Natural Language Processing, YOLOv4, Google Gemini, MeaningCloud, Wikipedia library

Real time electronic-waste classification algorithms using the computer vision based on Convolutional Neural Network (CNN): Enhanced environmental incentives

An innovative approach is needed to boost the economic value of e-waste by improving metal recovery and facilitating the separation of plastics and valuable metal components. Leveraging deep learning and computer vision technology offers a promising solution for automatically categorizing and sorting e-waste components like copper, printed circuit boards (PCB), steel, glass, and aluminum, presenting significant financial and environmental incentives for increased recycling efforts. In this instance, the real-time object identification algorithms YOLO 7 and 5 were used along with TensorFlow version 2.8.0. The e-waste dataset works incredibly well with TensorFlow and YOLO. For the detection of copper, PCBs, and plastic, the F1 values (F1 score is the harmonic mean of precision and recall; precision is the fraction of relevant instances among the retrieved instances whereas recall or sensitivity is the fraction of relevant instances that were retrieved) were as good as 1.0, whereas the score for steel and aluminum was 0.8. The mean average precision (mAP; for a set of queries, it is the mean of the average precision scores for each query) was 0.96 for all classes, with the highest precision for copper (0.99) followed by PCB (0.981). 240 e-waste objects were independently evaluated using the YOLO v7 model, achieving a remarkable ∼94 % prediction accuracy with a batch size of 16, ensuring robust performance. The real-time e-waste component detection was also done using video clips as well as webcam streaming. Deploying real-time e-waste object detection and sorting can significantly narrow the gap between e-waste accumulation and recycling rates, leading to reduced environmental strain and impact.

Object Detection and Recognition Using Image Processing

Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Region-based CNNs (R-CNNs) have transformed real-time object identification by processing images rapidly while maintaining high accuracy. These models use anchor boxes and pyramid networks to recognize objects at various scales and aspect ratios.

Object Detection using an Advanced Deep Learning Algorithm: YOLOv4

This research proposes implementing YOLOv4, a real-time object detection system that can accurately and efficiently recognize objects in photos and videos. The goals include creating the YOLOv4 architecture, generating datasets, training the model, evaluating its performance, deploying it in real-world applications, and providing extensive documentation. Deep learning frameworks such as TensorFlow or PyTorch are used in the implementation, along with advanced techniques such as transfer learning and data augmentation. By curating annotated datasets and refining training techniques, the model hopes to attain high accuracy, precision, and recall in object detection tasks. Performance analysis will compare the model's outcomes to those of cutting-edge systems, assessing its strengths and weaknesses. The deployment phase involves integrating the model into existing systems or creating separate applications for real-world scenarios such as pedestrian and vehicle detection. Comprehensive documentation and a user guide will help developers and users make the best use of the trained model. Overall, this research intends to demonstrate YOLOv4's usefulness and feasibility in developing computer vision technology and supporting the creation of intelligent systems with real-time object identification capabilities.

A modified generative adversarial networks with Yolov5 for automated forest health diagnosis from aerial imagery and Tabu search algorithm.

Our environment has been significantly impacted by climate change. According to previous research, insect catastrophes induced by global climate change killed many trees, inevitably contributing to forest fires. The condition of the forest is an essential indicator of forest fires. Analysis of aerial images of a forest can detect deceased and living trees at an early stage. Automated forest health diagnostics are crucial for monitoring and preserving forest ecosystem health. Combining Modified Generative Adversarial Networks (MGANs) and YOLOv5 (You Only Look Once version 5) is presented in this paper as a novel method for assessing forest health using aerial images. We also employ the Tabu Search Algorithm (TSA) to enhance the process of identifying and categorizing unhealthy forest areas. The proposed model provides synthetic data to supplement the limited labeled dataset, thereby resolving the frequent issue of data scarcity in forest health diagnosis tasks. This improvement enhances the model's ability to generalize to previously unobserved data, thereby increasing the overall precision and robustness of the forest health evaluation. In addition, YOLOv5 integration enables real-time object identification, enabling the model to recognize and pinpoint numerous tree species and potential health issues with exceptional speed and accuracy. The efficient architecture of YOLOv5 enables it to be deployed on devices with limited resources, enabling forest-monitoring applications on-site. We use the TSA to enhance the identification of unhealthy forest areas. The TSA method effectively investigates the search space, ensuring the model converges to a near-optimal solution, improving disease detection precision and decreasing false positives. We evaluated our MGAN-YOLOv5 method using a large dataset of aerial images of diverse forest habitats. The experimental results demonstrated impressive performance in diagnosing forest health automatically, achieving a detection precision of 98.66%, recall of 99.99%, F1 score of 97.77%, accuracy of 99.99%, response time of 3.543ms and computational time of 5.987ms. Significantly, our method outperforms all the compared target detection methods showcasing a minimum improvement of 2% in mAP.

Empowering Independence through Real Time Object Identification and Navigation for People with Disabilities

Recent studies in assistive technologies for visually impaired individuals showcase a diverse range of methodologies, algorithms, and implementations aimed at enhancing their independence. A notable focus revolves around leveraging cutting-edge technologies such as YOLO (You Only Look Once), SSD (Single Shot Multibox Detector), and Faster R-CNN (Region-based Convolutional Neural Network) to develop real-time object detection systems and deep learning-based smartphone navigation solutions . One prevalent theme in these advancements is the incorporation of auditory feedback to facilitate enhanced user interaction. This is achieved through sophisticated text-to-speech conversion and the integration of audio cues. The utilization of auditory cues not only aids in real-time awareness of the surroundings but also significantly contributes to the overall user experience . Despite remarkable progress, challenges persist in the realm of assistive technologies for the visually impaired. Issues such as processing speed, the occurrence of false positives and negatives, and the adaptability of these systems to various environmental conditions remain prominent. These challenges underline the need for continued research and development in this field to address existing limitations and refine the effectiveness of these assistive technologies .In essence, this survey provides a comprehensive understanding of the current landscape of assistive technologies for the visually impaired. By identifying both achievements and existing challenges, it serves as a valuable resource for researchers and practitioners, contributing to ongoing advancements that ensure tailored solutions and improved independence for individuals with visual impairments

Towards Industry 4.0: Color-Based Object Sorting Using a Robot Arm and Real-Time Object Detection

With the introduction of Industry 4.0, automation and robotics have made great strides, enabling 
 enterprises to improve their manufacturing processes for increased productivity and efficiency. This project 
 introduces a novel method for implementing Industry 4.0 concepts through color-based object sorting employing 
 a robot arm with real-time object identification capabilities. Creating a reliable and effective system that can 
 automatically categorize items based on their color properties is the main goal of this project. To enable seamless 
 object recognition and manipulation in real time, the suggested system integrates robotic manipulation with 
 computer vision algorithms. The system makes use of a convolutional neural network (CNN) for precise object 
 detection, using recent advancements in deep learning and image processing, allowing the robot arm to interact 
 with a variety of items effectively. The training phase and the sorting phase are the two key phases of the 
 approach. The CNN model is trained on a sizable dataset of labeled objects during the training phase to recognize 
 various colors and forms. In order for the robotic arm to recognize things as they go along the conveyor belt and 
 sort them into predetermined bins according to their respective colors, the trained model must be integrated with 
 the robotic arm during the sorting phase. Several experiments are carried out with various lighting setups and 
 object arrangements to evaluate the performance of the suggested system. The outcomes show how well the 
 system performs in terms of exact object detection and reliable sorting. The system's capacity to effectively 
 handle a variety of objects and adapt to changing environmental conditions further emphasizes its suitability for 
 use in actual industrial scenarios. This project has important ramifications for the manufacturing sector, enabling 
 improved automation capabilities and cost-efficiency. An important step towards implementing Industry 4.0 
 principles is the seamless integration of color-based object sorting and real-time object detection using a robotic 
 arm. This will allow industries to optimize their production processes, minimize human intervention, and 
 increase overall productivity. Further developments in robotics and computer vision are anticipated to push the 
 limits of automation and open the door for more advanced and intelligent industrial systems as technology 
 develops.

Deep Learning Approach: YOLOv5-based Custom Object Detection

Object detection is of significant importance in the field of computer vision, since it has extensive applications across many sectors. The emergence of YOLO (You Only Look Once) has brought about substantial changes in this domain with the introduction of real-time object identification with exceptional accuracy. The YOLOv5 architecture is highly sought after because of its increased flexibility and computational efficiency. This research provides an in-depth analysis of implementing YOLOv5 for object identification. This research delves deeply into the architectural improvements and design ideas that set YOLOv5 apart from its predecessors to illuminate its unique benefits. This research examines the training process and the efficiency of transfer learning techniques, among other things. The detection skills of YOLOv5 may be greatly improved by including these features. This study suggests the use of YOLOv5, a state-of-the-art object identification framework, as a crucial tool in the field of computer vision for accurate object recognition. The results of the proposed framework demonstrate higher performance in terms of mAP (60.9%) when evaluated with an IoU criterion of 0.5 and when compared to current methodologies in terms of reliability, computing flexibility, and mean average precision. These advantages make it applicable in many real-world circumstances.

Java Based Object Recognition Application for Visually Impaired

It was difficult to detect items in real time and turn them into an auditory output. Many real-time object identification apps have been made possible by recent developments in computer vision. This essay explains a basic Android application. that would make it easier for those who are blind to comprehend their surroundings. Data about the surrounding area was recorded using the phone's camera. where the object detection API of Tensor flow was used to perform real-time object recognition. Android's text-to-speech feature was then used to turn the objects that were recognized into an audio output. Complex algorithm offline processing is now easy thanks to Tensor flow lite. It was discovered that the suggested system's overall accuracy was close to 90%. Keywords- computer vision, machine learning, TensorFlow, TensorFlow Lite, object acknowledgment, and Android.

Dual-band real-time object identification via polarization reversal based on 2D GeSe image sensor

Dual-band real-time object identification via polarization reversal based on 2D GeSe image sensor

Realtime Object Detection and Disease Prediction of Visually Impaired People

In order to detect objects, this research combines real-time object identification with appropriate deep learning techniques. In our study, we describe the creation of a real-time system for item recognition, classification, and position estimation in the open environment. Blind and visually impaired people daily deal with a variety of difficulties. The proposed plan's goal. Computer vision is used to precisely identify indoor items. Those who are blind or visually challenged can use navigational aids, The Technology for navigation of the blind is not sufficiently accessible, without vision it can be challenging for visually impaired persons to navigate through rooms or different road paths .The main aim to develop the project is to help the visually impaired people and to detect the obstacles .The blind persons life become easier and without anyone helps they can walk alone through street they does not need anyone to assist them they can handle their self correctly. The preventing users from dangerous location our aim is to collected from environment (cameras, sensors, scanners, etc.) and transmitted to the users to the audio format. Data in the healthcare industry consists of all the information related to patients. Herea general architecture has been proposed for predicting the disease in the healthcare industry

Drishti : Real-Time Object Recognition for the Visually Impaired

In 2017, the World Health Organization (WHO) reported that nearly 284 million individuals worldwide experienced some degree of visual impairment, with approximately 39 million individuals suffering from total blindness. People with visual impairments often rely on assistance from others or use canes to move around and identify obstacles. Our proposed system aims to aid the visually impaired by identifying and classifying common objects in real-time, as well as recognizing text from various sources such as documents and signs. This system provides voice feedback to enhance understanding and navigation, and utilizes depth estimation algorithms to determine a safe distance between objects and individuals, promoting self-sufficiency and reducing dependence on others. We employ the COCO image dataset, which contains everyday objects and people, and utilize the Mobilenet SSD algorithm for real-time object identification. To enable real-time Optical Character Recognition (OCR) Text-To-Speech functionality, we employ advanced technologies such as OpenCV, Python, and Tesseract for text detection and recognition, and the Pyttsx3 library for converting recognized text into audible speech. Our proposed system is dependable, affordable, realistic, and feasible.

Real-time vehicular accident prevention system using deep learning architecture

Approximately 20 million people die every year of road accidents, mainly caused due to ignorance of road safety norms and traffic rules. Drivers’ experience and prudence still have to be relied upon to prevent vehicle accidents. Here, an algorithm is developed to prevent T-bone accidents, rear-end and head-on collisions, pedestrians accidents. Using Raspberry Pi 4 Model B with 8 GB RAM as processing unit, importing OpenCV library into python 3; an intensive dynamic real-time algorithm for the effective rear end and adjacent collision avoidance module is developed on the framework of deep learning architecture(DLA) for image categorization. In this work, real-time object identification, distance estimation, and tracking of the instantaneous position are performed in all environmental conditions using a deep learning algorithm without any additional sensors. The Raspberry Pi NoIR Camera Module V2 keeps sending the captured frames in real-time to feed the MobileNet DLA for Single Shot Multibox Detection (SSD). The attributes of the preceding obstructions are utilized in such a way that the live-distance between vehicles and other objects is approximated successfully by providing the instantaneous positional information whereby generating warning signal on exigency with a processing speed of 0.09 s per frame (11 fps). The real-time output data are analyzed by multivariate analysis and a 3x3x9 repeated measures ANOVA. The result shows that Multimodal Early Accident Alarming System is highly effective for distractive drivers.

Machine Learning Based Model Development with Annotated Database for Indian Specific Object Detection

<div class="section abstract"><div class="htmlview paragraph">Now-a-days, Advanced driver-assistance systems (ADAS) is equipping cars and drivers with advance information and technology to make them become aware of the environment and handle potential situations in better way semi-autonomously. High-quality training and test data is essential in the development and validation of ADAS systems which lay the foundation for autonomous driving technology.</div><div class="htmlview paragraph">ADAS uses the technology like radar, vision and combinations of various sensors including LIDAR to automatize dynamic driving tasks like steering, braking, and acceleration of vehicle for controlled and safe driving. And to integrate these advance technologies, the ADAS needs labeled data to train the algorithm to detect the various objects and moments of driver. Image annotation is one the well-known service to create such training data for computer vision.</div><div class="htmlview paragraph">There are number of open source annotated datasets available viz. COCO, KITTI etc. But these datasets are limited to either US or European road environment scenarios. There is hardly any dataset available for Indian Specific road conditions. In order to capture all the India Specific objects on road, we are capturing data through various environmental conditions, road conditions and annotating objects on the collected camera images. 2-D bounding boxes are being used to annotate objects in an image.</div><div class="htmlview paragraph">These annotated images will then be used to train a machine learning model. Finally this trained model will be then used to detect Indian specific objects with real time data acquired through camera sensors mounted on the vehicle. Object detection is one of the prime aspects for ADAS functionality. The complete process will demonstrate end to end solution for object detection techniques from data generation to real-time object identification and recognition.</div></div>

Adaptive Traffic Management System using CNN (YOLO)

The huge number of vehicles on the roadways is making congestion a significant problem. The line longitudinal vehicle waiting to be processed at the crossroads increases quickly, and the traditionally used traffic signals are not able to program it properly. Manual traffic monitoring may be an onerous job since a number of cameras are deployed over the network in traffic management centers. The proactive decision-making of human operators, which would decrease the effect of events and recurring road congestion, might contribute to the easing of the strain of automation.The traffic control frameworks in India are now needed as it is an open-loop control framework, without any input or detection mechanism. Inductive loops and sensors employed in existing technology used to detect the number of passing vehicles. The way traffic lights are adapted is highly inefficient and costly in this existing technology. The aim was to build a traffic control framework by introducing a system for detection ,which gives an input to the existing system (closed loop control system) in order to adapt to the changing traffic density patterns and to provide the controller with a crucial indication for ongoing activities. By this technique, the improvement of the signals on street is extended and thus saves time by preventing traffic congestion. This study proposes an algorithm for real-time traffic signal control, depending on the traffic flow. In reality, the features of competitive traffic flow at the signposted road crossing are used by computer vision and by machine learning. This is done by the latest, real-time object identification, based on convolutional Neural Networks network called You Look Once (YOLO). Traffic signal phases are then improved by data acquired in order to allow more vehicles to pass safely over minimal wait times, particularly the line long and the time of waiting per vehicle.This adjustable traffic signal timer is used to calculate traffic density utilizing YOLO object identification using live pictures of cameras in intervals and adjusts the signal timers appropriately, therefore decreasing the road traffic congestion, ensuring speedier transit for persons, and reducing fuel consumption. The traffic conditions will improve enormously at a relatively modest cost. Inductive loops are a viable but costly approach. This method thereby cuts expenses and outcomes quickly.

Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired

This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.

Mixing characterization of binary-coalesced droplets in microchannels using deep neural network.

Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time.

Cooperative unmanned aerial vehicles with privacy preserving deep vision for real-time object identification and tracking

Human tracking is an important challenge in a wide variety of applications, including but not limited to, surveillance, military operations, and disaster relief services. Unmanned Aerial Vehicles (UAVs) allow the surveying of dangerous or impassable areas from a safe distance. They also provide a machine-based capability, which may not only solve resource constraint issues, but can also improve effectiveness and efficiency in the tracking task. The effectiveness of tracking is directly related to the angle of view and degree of freedom of the camera system. In this paper, we introduce a decentralized, distributed deep learning algorithm for Real-Time Privacy-preserving Target Tracking Re-Identification (RPTT-ReID) used by cooperative UAVs in complex and adversarial environments involving motion, crowded scenes, and varied camera angles. The efficiency of RPTT-ReID makes it amenable to edge computing applications. The proposed algorithmic approach resolves shortfalls with current tracking algorithms, specifically challenges in maintaining tracking when subjects cross paths, switch identity, or are occluded in a frame of view. We demonstrate the power of our approach both in single and multi-UAV scenarios to track movable targets by extracting the facial embedding information in crowds, in order to ensure the privacy of individuals captured by the UAVs without compromising the capability for target re-identification. We validate RPTT-ReID on a challenging video dataset of crowded scenes. Our experimental evaluation shows that the proposed approach is capable of tracking and re-identifying people in crowds despite blended trajectories with minimum and maximum accuracy of 79.91 ± 0.2% and 93.27 ± 0.1% respectively. The proposed approach is 18% faster than previous methods for tracking in crowded urban environments.

Large-scale Real-time Object Identification Based on Analytic Features

Event Abstract Back to Event Large-scale Real-time Object Identification Based on Analytic Features Stephan Hasler1*, Heiko Wersing1 and Edgar Korner1 1 Honda Research Institute Europe GmbH, Germany Inspired by the findings that columns in inferotemporal cortex respond to complex visual features generalizing over retinal position and scale (Tanaka, Ann. Rev. of Neurosc., 1996) and that objects are then represented by the combined activation of such columns (Tsunoda et al., Nature Neurosc., 2001), we previously developed a framework to select a set of analytic SIFT-descriptors (Hasler et al., Proc. of ICANN, 2007), that is dedicated for 3D object recognition. In this work we embed this representation in an online system that is able to robustly identify a large number of pre-trained objects. In contrast to related work, we do not restrict the objects' pose to characteristic views but rotate them freely in hand in front of a cluttered background.To tackle this unconstrained setting we use following processing steps: Stereo images are acquired with cameras mounted on a pan-tilt unit. Disparity is used to select and track a region of interest based on closest proximity. To remove background clutter we learn a foreground mask using depth information as initial hypothesis (Denecke et al., Neurocomputing, 2009). Then analytic shape features and additional color features are extracted. Finally, the identification is performed by a simple classifier. To our knowledge, this is the first system that can robustly identify 126 hand-held objects in real-time.The used type of representation differs strongly from the standard SIFT framework proposed by Lowe (Int. J. of Comp. Vision, 2004). First, we extract SIFT-descriptors at each foreground position in the attended image region. Thus, parts are found to be analytic that would not have passed usual keypoint criteria. Second, we do not store the constellation of object parts but keep only the maximum response per feature. This results in a simple combinatorial object representation in accordance to biology, but depends on a good figure-ground segregation. Third, we match the local descriptors against an alphabet of visual features. This alphabet is rather small (usually several hundreds) and the result of a supervised selection strategy favoring object specific parts that can be invariantly detected in several object poses. The selection method is dynamic in the way, that it selects more features for objects with stronger variations in appearance. We draw a direct comparison to the SIFT framework using the COIL100 database as a toy-problem.Despite the quite simple object representation, our system shows a very high performance in distinguishing the 126 objects in the realistic online setting. We underline this by the tests on an offline database acquired under the same conditions. With a nearest neighbor classifier (NNC) we obtain an error rate of 25 percent using analytic features only. When adding an RGB histogram as complementary feature channel this error rate drops to 15 percent for the NNC and to 10.35 percent using a single layer perceptron. Considering the high difficulty of the database with a baseline NNC error rate of 85 percent on the gray-scale images compared to 10 percent for the COIL100, these results mark a major step towards invariant identification of 3D objects. Conference: Bernstein Conference on Computational Neuroscience, Frankfurt am Main, Germany, 30 Sep - 2 Oct, 2009. Presentation Type: Poster Presentation Topic: Abstracts Citation: Hasler S, Wersing H and Korner E (2009). Large-scale Real-time Object Identification Based on Analytic Features. Front. Comput. Neurosci. Conference Abstract: Bernstein Conference on Computational Neuroscience. doi: 10.3389/conf.neuro.10.2009.14.010 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 25 Aug 2009; Published Online: 25 Aug 2009. * Correspondence: Stephan Hasler, Honda Research Institute Europe GmbH, Offenbach, Germany, stephan.hasler@honda-ri.de Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Stephan Hasler Heiko Wersing Edgar Korner Google Stephan Hasler Heiko Wersing Edgar Korner Google Scholar Stephan Hasler Heiko Wersing Edgar Korner PubMed Stephan Hasler Heiko Wersing Edgar Korner Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.