Moving Object Detection Research Articles

An Improved Moving Object Detection in a Wide Area Environment using Image Classification and Recognition by Comparing You Only Look Once (YOLO) Algorithm over Deformable Part Models (DPM) Algorithm.

An Improved Moving Object Detection in a Wide Area Environment using Image Classification and Recognition by Comparing You Only Look Once (YOLO) Algorithm over Deformable Part Models (DPM) Algorithm.

Eventmd: High-Speed Moving Object Detection Based on Event-Based Video Frames

Eventmd: High-Speed Moving Object Detection Based on Event-Based Video Frames

FPGA implementation of multi-dimensional Kalman filter for object tracking and motion detection

Object tracking and motion detection are the major challenges in the real-time image and video processing applications. There are several tracking and prediction algorithms available to estimate and predict the state of a system. Kalman filter is the most widely used prediction algorithm as it is very simple, efficient and easy to implement for linear measurements. However, these types of filter algorithms are customized on hardware platforms such as Field-Programmable Gate Arrays (FPGAs) and Graphic Processing Units (GPUs) to achieve design requirements for embedded applications. In this work, a multi-dimensional Kalman filter (MDKF) algorithm is proposed for object tracking and motion detection. The numerical analysis of proposed tracking algorithm achieves competitive tracking performance in contrast with state-of-the-art tracking algorithms trained on standard benchmarks. Furthermore, MDKF is implemented on Xilinx Zynq™-7000 System-on-a chip (SoC). The implementation of MDKF on SoC performs 2× times tracking speed than that of software approach. The experimental results provide resource utilization of about 61.43% of Block RAMs (BRAMs), 90.09% of DSPs, 83.27% of Look-up tables (LUTs) and 82.35% of logic cells operating at 140 MHz with power consumption of 780 mW which outperforms previous related methods.

ARTFLOW: A Fast, Biologically Inspired Neural Network that Learns Optic Flow Templates for Self-Motion Estimation.

Most algorithms for steering, obstacle avoidance, and moving object detection rely on accurate self-motion estimation, a problem animals solve in real time as they navigate through diverse environments. One biological solution leverages optic flow, the changing pattern of motion experienced on the eye during self-motion. Here I present ARTFLOW, a biologically inspired neural network that learns patterns in optic flow to encode the observer’s self-motion. The network combines the fuzzy ART unsupervised learning algorithm with a hierarchical architecture based on the primate visual system. This design affords fast, local feature learning across parallel modules in each network layer. Simulations show that the network is capable of learning stable patterns from optic flow simulating self-motion through environments of varying complexity with only one epoch of training. ARTFLOW trains substantially faster and yields self-motion estimates that are far more accurate than a comparable network that relies on Hebbian learning. I show how ARTFLOW serves as a generative model to predict the optic flow that corresponds to neural activations distributed across the network.

A comparative study on SoC embedded low power GPUs for real‐time edge‐based automated traffic surveillance

SummaryAchieving real‐time processing for automated traffic surveillance is a major challenge due to the huge amount of data generated by a large number of surveillance cameras. For this, centralized computing has been a standard architecture for many years. But due to the increasing need for real‐time processing and limitations of centralized computing architecture (such as network congestion due to limited bandwidth and the need for costly high‐end servers), the paradigm is shifting toward edge processing. This article compares the suitability of two popular but different types of SoCs (System on Chip) (from NVIDIA and Qualcomm) embedded with low‐power GPUs as processing units for edge devices. These GPUs can be programmed as general‐purpose GPUs (GPGPU) to speed up compute‐intensive tasks, so as to achieve real‐time processing at the edge of the network. The article also discusses the architectural features and differences between these GPUs and recommends optimization techniques to leverage them. For quantitative comparison, we implement a wrong‐way vehicle detection algorithm (using background‐subtraction‐based moving object detection and Kalman‐filter‐based trajectory tracking), and optimized it for these SoCs. The experimental results show that real‐time processing can be achieved for HD videos with implementations optimized for these SoCs.

Improved Background Subtraction Technique for Detecting Moving Objects

Introduction: Moving object detection from videos is among the most difficult task in different areas of computer vision applications. Among the traditional object detection methods, researchers conclude that Background Subtraction method carried out better in aspects of execution time and output quality. Mehtod: Visual background extractor is a renowned algorithm in Background Subtraction method for detecting moving object in various applications. In the recent years, lots of work has been carried out to improve the existing Visual Background extractor algorithm. Result: After investigating many state of art techniques and finding out the research gaps, this paper presents an improved background subtraction technique based on morphological operation and 2D median filter for detecting moving object which reduces the noise in the output video and also enhances its accuracy at a very limited additional cost. Experimental results in several benchmark datasets confirmed the superiority of the proposed method over the state-of-the-art object detection methods. Conclusion: In this article, a method has been proposed for moving object detection where the quality of the output object is enhanced and good accuracy is achieved. This method provide with accurate experimental results, which helps in efficient object detection. The proposed technique also deals with Visual Background extractor Algorithm along with the Image Enhancement Procedure like Morphological and 2-D Filtering at a limited additional cost Discussion: This article worked on certain specific field, like noise reduction and image enhancement of output images of the existing ViBe Algorithm. The technique proposed in this article will be beneficial for various computer vision applications like video surveillance, road condition monitoring, airport safety, human activity analysis, monitoring marine border for security purpose etc.

Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

For traffic surveillance systems (TSS), Moving object detection and tracking is the key technology. On account of moving object-orientation variation, changing weather conditions, moving objects’ appearance, and non-target objects in the background, it's very hard to detection some objects in a traffic sequence of the video frame. Though many methods were introduced to detect and track moving objects in a traffic environment, they could not attain a desirable output. Hence, this work proposes an enhanced system for automatic moving object detection (MOD) and object tracking (OT) using RBF-FDLNN and CFR algorithms. The noises existent in a video frame are initially filtered using a GESFA filter. Next, the conversion of the RGB frame to HSL is done, and the background regions are eradicated from the frame using IGMM. After background removal (BGR), the Motion Estimation (ME) of object and OT are performed using GCS and CFR algorithm, respectively. Subsequently, the required features in the frames are extracted and fed into the RBF-FDLNN classifier for performing object detection (OD). The performance of the proposed method RBF-FDLNN classifier is better than other existing methods in the given video frame. Lastly, the outcomes are proffered to corroborate the proposed method’s effectiveness.

Long Distance Moving Vehicle Tracking with a Multirotor Based on IMM-Directional Track Association

The multirotor has the capability to capture distant objects. Because the computing resources of the multirotor are limited, efficiency is an important factor to consider. In this paper, multiple target tracking with a multirotor at a long distance (~400 m) is addressed; the interacting multiple model (IMM) estimator combined with the directional track-to-track association (abbreviated as track association) is proposed. The previous work of the Kalman estimator with the track association approach is extended to the IMM estimator with the directional track association. The IMM estimator can handle multiple targets with various maneuvers. The track association scheme is modified in consideration of the direction of the target movement. The overall system is composed of moving object detection for measurement generation and multiple target tracking for state estimation. The moving object detection consists of frame-to-frame subtraction of three-color layers and thresholding, morphological operation, and false alarm removing based on the object size and shape properties. The centroid of the detected object is input into the next tracking stage. The track is initialized using the difference between two nearest points measured in consecutive frames. The measurement nearest to the state prediction is used to update the state of the target for measurement-to-track association. The directional track association tests both the hypothesis and the maximum deviation between the displacement and directions of two tracks followed by track selection, fusion, and termination. In the experiment, a multirotor flying at an altitude of 400 m captured 55 moving vehicles around a highway interchange for about 20 s. The tracking performance is evaluated for the IMMs using constant velocity (CV) and constant acceleration (CA) motion models. The IMM-CA with the directional track association scheme outperforms other methods with an average total track life of 91.7% and an average mean track life of 84.2%.

Sifting through the Static: Moving Object Detection in Difference Images

Abstract Trans-Neptunian objects provide a window into the history of the solar system, but they can be challenging to observe due to their distance from the Sun and relatively low brightness. Here we report the detection of 75 moving objects that we could not link to any other known objects, the faintest of which has a VR magnitude of 25.02 ± 0.93 using the Kernel-Based Moving Object Detection (KBMOD) platform. We recover an additional 24 sources with previously known orbits. We place constraints on the barycentric distance, inclination, and longitude of ascending node of these objects. The unidentified objects have a median barycentric distance of 41.28 au, placing them in the outer solar system. The observed inclination and magnitude distribution of all detected objects is consistent with previously published KBO distributions. We describe extensions to KBMOD, including a robust percentile-based lightcurve filter, an in-line graphics-processing unit filter, new coadded stamp generation, and a convolutional neural network stamp filter, which allow KBMOD to take advantage of difference images. These enhancements mark a significant improvement in the readiness of KBMOD for deployment on future big data surveys such as LSST.

Video Desnowing and Deraining via Saliency and Dual Adaptive Spatiotemporal Filtering.

Outdoor vision sensing systems often struggle with poor weather conditions, such as snow and rain, which poses a great challenge to existing video desnowing and deraining methods. In this paper, we propose a novel video desnowing and deraining model that utilizes the salience information of moving objects to address this problem. First, we remove the snow and rain from the video by low-rank tensor decomposition, which makes full use of the spatial location information and the correlation between the three channels of the color video. Second, because existing algorithms often regard sparse snowflakes and rain streaks as moving objects, this paper injects salience information into moving object detection, which reduces the false alarms and missed alarms of moving objects. At the same time, feature point matching is used to mine the redundant information of moving objects in continuous frames, and a dual adaptive minimum filtering algorithm in the spatiotemporal domain is proposed by us to remove snow and rain in front of moving objects. Both qualitative and quantitative experimental results show that the proposed algorithm is more competitive than other state-of-the-art snow and rain removal methods.

Moving object detection in low-luminance images

Moving object detection in low-luminance images

An accurate foreground moving object detection based on segmentation techniques and optimal classifier

AbstractIn video surveillance schemes, the motion object detection plays a significant role. To subtract the object background, a segmentation technique based on feature extraction is utilized in which the change in the training rate makes an alteration in the background. Thereafter, the extracted features are trained by using the self‐organizing map (SOM) network in which the weight parameters in the network is optimized with the help of artificial bee colony (ABC) optimization algorithm, so, the proposed methodology is named as HSOM‐ABC technique. This methodology is carried out to perform the classification process in this research. Initially, the whole dataset is preprocessed with the help of grayscale conversion method which converts the original image into grayscale color. After this, fuzzy c‐means clustering is applied to perform the segmentation process and this method divides the foreground and background parts efficiently. Then, feature extraction is done with the help of local binary pattern method which extract the relevant features from the segmented image. Finally, HSOM‐ABC method is proposed to accurate classification process. Hence, the moving objects are identified by categorizing the background and foreground images. MatLab platform is chosen for the proposed work simulation and the performance is evaluated by means of different parameters and it is compared with new existing approaches. Experimental outcomes show that the proposed strategy achieves higher precision value than any other existing methods.

Moving Object Detection in Traffic Surveillance Video: New MOD-AT Method Based on Adaptive Threshold

Previous research on moving object detection in traffic surveillance video has mostly adopted a single threshold to eliminate the noise caused by external environmental interference, resulting in low accuracy and low efficiency of moving object detection. Therefore, we propose a moving object detection method that considers the difference of image spatial threshold, i.e., a moving object detection method using adaptive threshold (MOD-AT for short). In particular, based on the homograph method, we first establish the mapping relationship between the geometric-imaging characteristics of moving objects in the image space and the minimum circumscribed rectangle (BLOB) of moving objects in the geographic space to calculate the projected size of moving objects in the image space, by which we can set an adaptive threshold for each moving object to precisely remove the noise interference during moving object detection. Further, we propose a moving object detection algorithm called GMM_BLOB (GMM denotes Gaussian mixture model) to achieve high-precision detection and noise removal of moving objects. The case-study results show the following: (1) Compared with the existing object detection algorithm, the median error (MD) of the MOD-AT algorithm is reduced by 1.2–11.05%, and the mean error (MN) is reduced by 1.5–15.5%, indicating that the accuracy of the MOD-AT algorithm is higher in single-frame detection; (2) in terms of overall accuracy, the performance and time efficiency of the MOD-AT algorithm is improved by 7.9–24.3%, reflecting the higher efficiency of the MOD-AT algorithm; (3) the average accuracy (MP) of the MOD-AT algorithm is improved by 17.13–44.4%, the average recall (MR) by 7.98–24.38%, and the average F1-score (MF) by 10.13–33.97%; in general, the MOD-AT algorithm is more accurate, efficient, and robust.

Motion estimation in hazy videos

• A novel end-to-end deep network is proposed for haze removal from hazy video frames. • Failure of conventional approaches in estimating motion information in hazy videos has been analyzed. • A novel haze removal → motion estimation approach is proposed for motion estimation from hazy videos. Motion estimation is the basic need for the success of many video analysis algorithms such as moving object detection, human activity recognition, etc. Most of the motion estimation algorithms are prone to weather conditions and thus, they fail to estimate the motion in degraded weather. Severe weather situations like snow, rain, haze, smog, etc., degrades the performance and reliability of video analysis algorithms. In this paper, we have analyzed the effect of the haze on motion estimation in hazy videos. We propose a cascaded architecture i.e. haze removal followed by optical flow for motion estimation in hazy videos. The proposed image de-hazing network is build upon the Res idual and I nception module concepts and named as ResINet. Further, an optical flow is utilized to estimate the motion information. We have carried out the visual analysis to validate the proposed approach for motion estimation in hazy videos. Also, to validate the proposed ResINet for de-hazing, we carried out the quantitative analysis on two benchmark image de-hazing datasets.

Moving-object detection and tracking by scanning LiDAR mounted on motorcycle based on dynamic background subtraction

This paper presents a method for moving-object detection and tracking (DATMO) in global navigation satellite systems (GNSS)-denied environments using a light detection and ranging (LiDAR) mounted on a motorcycle. Distortion in the scanning LiDAR data is corrected by estimating the pose (3D positions and attitude angles) of the motorcycle in a period shorter than the LiDAR scan period using normal distributions transform-based simultaneous localization and mapping (NDT-based SLAM) and the information from an inertial measurement unit (IMU) via the extended Kalman filter (EKF). The scan data of interest are extracted by subtracting the local environment map generated by NDT-based SLAM from the LiDAR scan data. Moving objects are detected from the scan data of interest using an occupancy grid method and are tracked with a Bayesian filter. Experimental results obtained from public road and university campus environments demonstrate the effectiveness of the proposed method.

Detection of moving objects using adaptive multi-feature histograms

This paper presents a moving object detection scheme that incorporates three innovations. First, considering the inter-frame consistency of pixels, we extend the compact binary face descriptor (CBFD) to the temporal domain and propose a novel local binary descriptor named temporally-consistent local compact binary descriptor (TC-LCBD), which exploits the useful correlation of the intensities of inter-frame pixels to guarantee good performance in complex scenes. We do this mainly because the background scene between frames has a significant coherence. Second, both color and TC-LCBD features are modeled as a group of adaptive histograms for characterizing each pixel, which can enhance the robustness to dynamic backgrounds and illumination changes. Third, by comparing changes in histogram proximity between two adjacent frames, we can dynamically adjust the model sensitivity and adaptation rate without user intervention. Experimental results on well-known, challenging data sets demonstrate that the proposed method significantly outperforms many state-of-the-art methods.

Background subtraction for moving object detection: explorations of recent developments and challenges

Background subtraction for moving object detection: explorations of recent developments and challenges

An effective motion object detection using adaptive background modeling mechanism in video surveillance system

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

Moving Object Detection for Airport Scene Using Patterns of Motion and Appearance

This paper presents a novel method for localization and recognition of moving objects in a real airport surface scene. Different from the traditional applications, moving object detection (MOD) in the airport surface is more challenging because the background is an open outdoor environment, which means that the target objects are usually low in resolution and the MOD task is vulnerable to many undesired changes, such as cloud movement and illumination variations. To address these issues, this paper proposes a unified and effective deep-learning-based MOD architecture, which combines both appearance and motion cues. Specifically, a novel moving region proposal generation module is first designed, which can effectively locate the regions of moving object based on the motion information. Meanwhile, a novel cascade multilayer feature fusion module with transposed convolution is applied to produce both enriched-semantics and fine-resolution convolutional feature maps for category recognition. Finally, a large-scale dataset acquired by the daily surveillance videos of a real airport surface is manually constructed. Results show that the proposed methods outperform state-of-the-art solutions in extracting moving objects from airport surface scenes.

A Moving Object Detection and Predictive Control Algorithm Based on Deep Learning

In traffic scene, a low detection rate of dynamic target is caused by interference features of background area and fast speed of detected moving target. In this paper, an optimal target detection and prediction algorithm is proposed. Firstly, the algorithm of real-time motion parameters (speed, direction, etc.) detection of moving objects (vehicles) based on the depth theory is studied. The original prediction problem is transformed into the problem of automatic updating rate of uncertain parameters and the problem of minimizing the maximum path distance. Secondly, by on-line estimation of the automatic update rate, the proposed trajectory generated is guaranteed to be the minimum length. The allocation strategy minimizes the maximum distance traveled in the collision free. Then, the stability of the estimation error system is guaranteed base on deep learning algorithm. Finally, the simulation shows that the proposed algorithm can be used for moving target detection and path prediction accurately. The performance of the control system is improved.