Surveillance System For Detection Research Articles

Internet of things module accelerated dense deep learning for crime detection in surveillance systems

The smart surveillance system is becoming a vital application in each streets or houses. Most of the streets are prone to several misbehavior conducts for instance theft in atm, robbery, fights etc. and hence it is necessary to detect and analyse the crime scenes for finding the suspects. However, most of the surveillance system suffers from poor detection of objects due to poor camera resolution, absence of light and other factors. In order to improve the detection of faces after detecting the objects using ResNet, it is necessary to adapt some advanced devices for image capturing and analyzing. In this paper, Internet of Things (IoT) based ESP32 CAM WiFi Module Bluetooth with OV2640 Camera Module 2MP is used for image acquisition that capture better images from the scenes. The study uses dense convolutional network namely DenseNet to detect the faces present in the crime scenes after the object detection. The deep learning module is trained with selected crime scenes for training the classifier. The simulation is conducted further to validate the model with other variants of deep learning.

OSDDY: embedded system-based object surveillance detection system with small drone using deep YOLO

Computer vision is an interdisciplinary domain for object detection. Object detection relay is a vital part in assisting surveillance, vehicle detection and pose estimation. In this work, we proposed a novel deep you only look once (deep YOLO V3) approach to detect the multi-object. This approach looks at the entire frame during the training and test phase. It followed a regression-based technique that used a probabilistic model to locate objects. In this, we construct 106 convolution layers followed by 2 fully connected layers and 812 × 812 × 3 input size to detect the drones with small size. We pre-train the convolution layers for classification at half the resolution and then double the resolution for detection. The number of filters of each layer will be set to 16. The number of filters of the last scale layer is more than 16 to improve the small object detection. This construction uses up-sampling techniques to improve undesired spectral images into the existing signal and rescaling the features in specific locations. It clearly reveals that the up-sampling detects small objects. It actually improves the sampling rate. This YOLO architecture is preferred because it considers less memory resource and computation cost rather than more number of filters. The proposed system is designed and trained to perform a single type of class called drone and the object detection and tracking is performed with the embedded system-based deep YOLO. The proposed YOLO approach predicts the multiple bounding boxes per grid cell with better accuracy. The proposed model has been trained with a large number of small drones with different conditions like open field, and marine environment with complex background.

Adaptive Technique for Brightness Enhancement of Automated Knife Detection in Surveillance Video with Deep Learning

Detecting knives in surveillance videos are very urgent for public safety. In general, the research in identifying dangerous weapons is relatively new. Knife detection is a very challenging task because knives vary in size and shape. Besides, it easily reflects lights that reduce the visibility of knives in a video sequence. The reflection of light on the surface of the knife and the brightness on its surface makes the detection process extremely difficult, even impossible. This paper presents an adaptive technique for brightness enhancement of knife detection in surveillance systems. This technique overcomes the brightness problem that faces the steel weapons and improves the knife detection process. It suggests an automatic threshold to assess the level of frame brightness. Depending on this threshold, the proposed technique determines if the frame needs to enhance its brightness or not. Experimental results verify the efficiency of the proposed technique in detecting knives using the deep transfer learning approach. Moreover, the most four famous models of deep convolutional neural networks are tested to select the best in detecting knives. Finally, a comparison is made with the-state-of-the-art techniques, and the proposed technique proved its superiority.

RETRACTED ARTICLE: An improved hybridized deep structured model for accurate video event recognition

Video event recognition plays an important role in the various research fields particularly in surveillance detection system. In the existing system it is done by deep hierarchical context model which utilizes several contextual data which contains various context information at level of feature, semantic and priority for the video recognition process. However, this research method might perform low with increased volume of videos and might be failed to predict the events accurately with less interrelation contextual features. The standstill challenges are solved via improved hybridized deep structured model. The three primary features of contextual data are to discriminate resultant neighborhood. Here hybrid textual perceptual descriptor and concept-based attribute extraction is performed for accurate recognition of video events. These extracted interaction context features are grouped by using improved K means algorithm. In addition, improved deep structured model that combines convolutional neural networks and conditional random fields are developed for learning middle level representations and mingle the bottom feature level, mid-semantic and top-level meanings for the identification of incidents. This proposed research method is evaluated by using VIRAT data set whose simulation analysis is performed using Matlab simulation toolkit. The overall evaluation of the proposed research method proves that the suggested method can provide better output in terms of accurate recognition of events.

Non-Temporal Lightweight Fire Detection Network for Intelligent Surveillance Systems

Convolutional neural networks (CNNs) have been recently applied to tackle a variety of computer vision problems. However, because of its high computational cost, careful considerations are required to design cost-effective CNNs. In this paper, we propose a CNN inspired by MobileNet for fire detection in surveillance systems. In the proposed network, color features emphasized by the channel multiplier are extracted through depthwise separable convolution, and squeeze and excitation modules further increase the representation of the channel-wise convolution. Custom Swish is used as an activation function to limit exceedingly high weights from the effects of the channel multiplier. Our proposed network achieves 95.44% accuracy for fire detection, which is higher than those achieved other existing networks. Furthermore, the number of parameters used is 38.50% fewer than that of MobileNetV2, the smallest among other networks. We believe that using the proposed CNN, CNN-based surveillance systems could be implemented in lightweight devices without using expensive dedicated processors.

Genetic Algorithm based Vehicle Detection in Surveillance system

Durg Abstract— Traffic surveillance is used by private company, government and public organization for efficient management of transport network road safety, public safety in highways and busy street. A static camera observing a scene is an ordinary case of a surveillance system. Traffic surveillance is used by private company, government and public organization for efficient management of transport network road safety, public safety in highways and busy street. A static camera observing a scene is an ordinary case of a surveillance system. Vehicle classification is an inherently difficult problem. The tracked vehicles are classified into two categories: cars and non-cars. The accuracy of the system was impressive, but they only used lateral views of the vehicles for testing. Their results are very good, but they also limited their testing to lateral views. They have produced impressive results, but the question of retrieving the ―linearity‖ feature in frontal view remains unanswered. The Proposed Vehicle Detection technique using Genetic Algorithm performed the Cross over with the help of 1 point crossover and 2 Point crossover with random variables, which increased the speed of detection of the vehicles and accuracy whereas the previous works included tournament method. This method is very useful in detecting a large number of vehicles, and it also decreased the processing time, which was a major issue in the previous works. Keywords - Traffic surveillance, Vehicle Classification, Genetic algorithm, 1 point crossover, 2 point crossover and tournament method.

Human Detection Framework for Automated Surveillance Systems

Vision-based systems for surveillance applications have been used widely and gained more research attention. Detecting people in an image stream is challenging because of their intra-class variability, the diversity of the backgrounds, and the conditions under which the images were acquired. Existing human detection solutions suffer in their effectiveness and efficiency. In particular, the accuracy of the existing detectors is characterized by their high false positive and negative. In addition, existing detectors are slow for online surveillance systems which lead to large delay that is not suitable for surveillance systems for real-time monitoring. In this paper, a holistic framework is proposed for enhancing the performance of human detection in surveillance system. In general, the framework includes the following stages: environment modeling, motion object detection, and human object recognition. In environment modeling, modal algorithm has been suggested for background initialization and extraction. Then for effectively classifying the motion object, edge detecting and B-spline algorithm have been used for shadow detection and removal. Then, enhanced Lucas–Kanade optical flow has been used to get the area of interest for object segmentation. Finally, to enhance the segmentation, some morphological processes were performed. In the motion object recognition stage, segmentation for each blob is performed and processed to the human detector which is a complete learning-based system for detecting and localizing objects/humans in images using mixtures of deformable part models (PFF detector). Results show enhancement in each phase of the proposed framework. These enhancements are shown in the overall performance of human detection in surveillance system.

Efficient and effective automated surveillance agents using kernel tricks

Many schemes have been presented over the years to develop automated visual surveillance systems. However, these schemes typically need custom equipment, or involve significant complexity and storage requirements. In this paper we present three software-based agents built using kernel machines to perform automated, real-time intruder detection in surveillance systems. Kernel machines provide a powerful data mining technique that may be used for pattern matching in the presence of complex data. They work by first mapping the raw input data onto a (often much) higher-dimensional feature space, and then clustering in the feature space instead. The reasoning is that mapping onto the (higher-dimensional) feature space enables the comparison of additional, higher-order correlations in determining patterns between the raw data points. The agents proposed here have been built using algorithms that are adaptive, portable, do not require any expensive or sophisticated components, and are lightweight and efficient having run times of the order of hundredths of a second. Through application to real image streams from a simple, run-of-the-mill closed-circuit television surveillance system, and direct quantitative performance comparison with some existing schemes, we show that it is possible to easily obtain high detection accuracy with low computational and storage complexities.

Wavelet based outlier correction for power controlled turning point detection in surveillance systems

Detection turning points in unimodel has various applications to time series which have cyclic periods. Related techniques are widely explored in the field of statistical surveillance, that is, on-line turning point detection procedures. This paper will first present a power controlled turning point detection method based on the theory of the likelihood ratio test in statistical surveillance. Next we show how outliers will influence the performance of this methodology. Due to the sensitivity of the surveillance system to outliers, we finally present a wavelet multiresolution (MRA) based outlier elimination approach, which can be combined with the on-line turning point detection process and will then alleviate the false alarm problem introduced by the outliers.

Weaving a proper net to catch large objects in wireless sensor networks

Wireless sensor networks consist of a large number of sensors and have been broadly used for intrusion detection in surveillance systems. To guarantee detection quality, such networks are usually over-engineered, i.e., more than required sensors are deployed and remain active in order to cover each point in the monitored field with a high probability at any time instance. Existing sensor scheduling schemes based on the point coverage model tightly weave a sensor that is unnecessarily dense. Intuitively, when the size and the shape of intrusion objects are considered, any net with holes no smaller than the size of the intrusion object would work fine. With this design philosophy in mind, we build a new mathematical model to investigate the impact of size and shape of intrusion objects on network configuration. We derive analytical results that provide practitioners with insights on how to weave an effective sensor for intrusion object detection with minimum number of active sensors.