Compressed sensing based background subtraction (CS-BS) plays a significant role in video surveillance applications in Wireless Visual Sensor Networks. This paper implements a CS-BS framework with a novel thresholding strategy to detect the anomaly with fewer measurements in a secured indoor environment. In CS-BS, the CS is performed on the difference frame which is sparse, thereby reducing energy, memory and bandwidth. In this framework, a foreground threshold is proposed based on the measurement matrix to extract the moving object from a scene. The performance of the CS-BS framework with FDV is evaluated using metrics such as detection accuracy, energy complexity, percentage of reduction in samples and measurements. The proposed CS-BS framework with hybrid matrix based FDV achieves around 95.8% reduction of measurements and 91% reduction of samples.

Compressed sensing based foreground detection vector for object detection in Wireless Visual Sensor Networks

Smart traffic and information systems require the collection of traffic data from respective sensors for regulation of traffic. In this regard, surveillance cameras have been installed in monitoring and control of traffic in the last few years. Several studies are carried out in video surveillance technologies using image processing techniques for traffic management. Video processing of a traffic data obtained through surveillance cameras is an instance of applications for advance cautioning or data extraction for real-time analysis of vehicles. This paper presents a detailed review of vehicle detection and classification techniques and also discusses about different approaches detecting the vehicles in bad weather conditions. It also discusses about the datasets used for evaluating the proposed techniques in various studies.

Vehicle Detection and Classification: A Review

Limited memory, energy and bandwidth are the major constraints in wireless visual sensor network (WVSN). Video surveillance applications in WVSN attracts a lot of attention in recent years which requires high detection accuracy and increased network lifetime that can be achieved by reducing the energy consumption in the sensor nodes. Compressed sensing (CS) based background subtraction plays a significant role in video surveillance application for detecting the presence of anomaly with reduced complexity and energy. This paper presents a system based on CS for single and multi object detection that can detect the presence of an anomaly with higher detection accuracy and minimum energy. A novel and efficient mean measurement differencing approach with adaptive threshold strategy is proposed for detection of the objects with less number of measurements, thereby reducing transmission energy. The performance of the system is evaluated in terms of detection accuracy, transmission energy and network lifetime. Furthermore, the proposed approach is compared with the conventional background subtraction approach. The simulation results show that the proposed approach yields better detection accuracy with 90% reduction in samples compared to conventional approach.

Efficient compressed sensing based object detection system for video surveillance application in WMSN

Video surveillance applications in wireless visual sensor networks (WVSN) attract a lot of attention in recent years which demands higher performance with less complexity. Efficient and simple moving object detection and tracking (MODT) system is presented in this paper targetting the video surveillance application in WVSN. The main contribution of this paper is to develop a system that can perform both object detection and tracking with less complexity. This MODT system adopts compressed sensing (CS) to perform the background subtraction on compressive measurements and the subtracted measurements undergoes a measurement selection process (MSP) to extract the foreground measurements. MSP aims at extracting the minimum number of measurements that can yield higher detection accuracy. The detected object is tracked using the kalman filtering approach. Initially the centroid of the object is extracted from the binary image with the help of contour tracing which is then given as input to the Kalman filter to track the objects in the video. The performance of the MODT system is evaluated using parameters such as percentage of reduction in samples, energy complexity, detection and tracking accuracy. From the results it is evident that MODT system can achieve higher detection and tracking accuracy by reducing the measurements to more than 83 %. Also, the significance of the extracted measurements is observed by analyzing the detection accuracy which is around 0.88.

Compressed sensing based object detection and tracking system using measurement selection process for wireless visual sensor networks

The invention discloses an intelligent video monitoring system based on a DirectShow technology. The intelligent video monitoring system comprises a DirectShow component module, a video display set module, a motion object detection module and a data coding compression module, wherein the DirectShow component module is used for setting up an original video flow link, capturing an original video flow, and dividing the original video flow; the video display set module is used for setting an image display picture format, and returning the image display picture format to the DirectShow component module to display in real time; the motion object detection module is used for detecting a motion target in a monitoring area, transmitting image data including the motion target to the DirectShow component module to store the image data as a video file and give an alarm, and otherwise, automatically abandoning image data not including the motion target; and the data coding compression module is used for performing subsequent coding compression and storage of the video file stored by the DirectShow component module, and deleting the original video file. According to the intelligent video monitoring system disclosed by the invention, the technical problem that a video monitoring system, which is simple to develop and relatively high in accuracy, is lacking in engineering practices can be solved; and furthermore, the intelligent video monitoring system has the advantages of being low in cost, rapid in calculation speed and better in real-time performance.

Intelligent video monitoring system based on DirectShow technology

A Vehicle Detection Algorithm Based on Compressive Sensing and Background Subtraction

Provided is a real-time transport vehicle detecting and tracking method. According to the real-time transport vehicle detecting and tracking method, in moving vehicle real-time detection, vehicle traveling areas are obtained by utilizing road line detection, main color information of moving areas and main color information of non-moving areas in the vehicle traveling areas are obtained by carrying out frame subtraction, the moving vehicle areas are filled with the main color information to obtain an approximate background image, and finally moving vehicles are obtained by utilizing background subtraction. In moving vehicle tracking, all feature angle points of the moving vehicles are obtained by utilizing Harris detection, feature angle point sets of all separated moving areas are obtained through clustering analysis, each feature angle point set generates a feature circle containing all feature points, problems of vehicle blocking and the like are analyzed by using radiuses of the feature circles, and finally feature matching tracking is carried out by using circle centers of the feature circles. According to the real-time transport vehicle detecting and tracking method, moving vehicle detection, instantaneity of tracking and tracking precision are all improved greatly, and the real-time transport vehicle detecting and tracking method has wide application significance.

Real-time transport vehicle detecting and tracking method

A new quaternion convolutional attention denoising network known as DeQCANet for color random-valued impulse noise removal is proposed in this paper. First, a structural information extraction block based on a dilated convolution operation is designed to extract the structure and detail feature map. Subsequently, a quaternion convolutional neural network with a new quaternion map construction strategy is implemented to gain the color features across channels further. Finally, to integrate the global and local features, a feature enhancement block based on the attention mechanism is proposed to guide the network for impulse noise denoising. The experimental results demonstrate that the proposed denoising technique exhibits competitive performance compared to other well-known color image denoising methods.

Color Random Valued Impulse Noise Removal Based on Quaternion Convolutional Attention Denoising Network

This paper proposed methods of vehicle detection and tracking algorithm in real-time traffic. In the detection of realtime moving vehicle, vehicle areas would be determined through road line detection. Then, the main color information of moving and non-moving area would be obtained through frame difference. Filling the main color information in vehicle moving area would lead to a similar background image. At last, moving vehicles would be determined through adaptive Background Subtraction difference. In the tracking of moving vehicles, firstly, all characteristic corners can be got by using Harris detection. Then, all characteristic corner set in the separate moving area would be collected through cluster analysis. For each characteristic individual corner set can generate a circle embracing all characteristics, some problems like vehicle barrier could be analyzed by using the radius of characteristic circle. At last, conduct feature matching tracking by using the center of feature circle. Experimental result shows that the improving algorithm can extract all moving objects, which was endowed with strong background adaptability and better real-time performance. Keywords-target detection; frame difference method; background subtraction difference method; harris corner detection; clustering analysis

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Research on Vehicle Detection and Tracking Algorithm Based on the Methods of Frame Difference and Adaptive Background Subtraction Difference

Improved binary particle swarm optimization algorithm with experience factor: Improved binary particle swarm optimization algorithm with experience factor

Yiqin Cao

Papers

A Vehicle Detection Algorithm Based on Compressive Sensing and Background Subtraction

Real-time transport vehicle detecting and tracking method

Color Random Valued Impulse Noise Removal Based on Quaternion Convolutional Attention Denoising Network

Research on Vehicle Detection and Tracking Algorithm Based on the Methods of Frame Difference and Adaptive Background Subtraction Difference

Improved binary particle swarm optimization algorithm with experience factor: Improved binary particle swarm optimization algorithm with experience factor