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This paper presents TelosCAM, a sensor-camera network system that can track and identify burglars while ensuring privacy protection. The system integrates wireless sensor nodes with surveillance cameras to detect events and trigger tracking processes. It also features privacy-aware triggering schemes, trajectory-based video extraction, and collaborative burglar identification through networked cameras. The evaluation results show the effectiveness and efficiency of the TelosCAM system.
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TelosCAM: Identifying Burglar Through Networked Sensor-Camera Mates with Privacy Protection Presented by Qixin Wang Shaojie Tang,Xiang-Yang Li, Haitao Zhang Jiankang Han, Guojun Dai, Cheng Wang, Xingfa Shen
Introduction • Video surveillance are already widely used in Airports, Border, Railways, Underground, and Roadway… • Wireless Sensors also attract significant attention these days for event monitoring and communication…
What is Sensor-Camera Network? • Integrates wireless module nodes (such as TelosB nodes) with legacy surveillance cameras • Wireless module node is able to detect event and trigger tracking process • Surveillance camera is used to capture video of interest
Why is Sensor-Camera Network? • TelosCAM system is able totrack and identify the burglar who stole the property • TelosCAM has the following advantages: Privacy Protection High Reliability Camera Storage Efficiency Light Modification
Design Requirement • Reliable • Energy Efficient • Privacy Aware • Storage Efficient • High Hitting Ratio • Computation Efficient • High Accuracy
Privacy Aware Triggering Scheme I The tracking process is triggered if the property is out of transmission range of the owner. • Naïve triggering scheme may cause serious privacy issue
Privacy Aware Triggering Scheme II We thus design a secured message exchanging scheme to prevent potential attacking. However, the above design suffers from potential security issues if not designed carefully.
TRAJECTORY BASED VIDEO EXTRACTION I • Video extraction aims to archive only those videos which contain the burglar with high probability . • When a burglar passes through a surveillance point, the surveillance wireless module (s) will receive some alarm messages sent by the secondary wireless module. • A naive scheme is to let each camera start extract the video once they detect the appearance of the object, however, it suffers from poor storage efficiency.
TRAJECTORY BASED VIDEO EXTRACTION II • We propose a trajectory based extraction scheme to ensure high storage efficiency without scarifying reliability. • The basic idea is to • Reconstruct burglar’ trajectory based on sensing result; • Estimate the time when the object entered and left the camera sensing range; • 3. Filter out those videoswhich are less likely to contain the burglar based on above information.
Burglar Identification Through Video Processing I To identify the burglar from set of retrieved videos. Among all the objects ever appeared in the extracted videos from all relevant cameras, the burglar tends to have the most occurrences.
Burglar Identification Through Video Processing II Step I. Suspicious Objects Selection From Single Camera Object Classification
Burglar Identification Through Video Processing II Step I. Suspicious Objects Selection From Single Camera We select top-k objects with longest appearance durations as k most suspicious objects for each video.
Burglar Identification Through Video Processing II Step II. Collaborative Burglar Identification Through Networked Cameras Inter-Camera Calibration: to construct pair wise camera color mapping that maps the color histogram from one camera to the other. We formulate the mapping problem as a maximum weighted matching problem A histogram, h, is a vector {h[0], … , h[N]} in which each bin h[i] contains the percentage of pixels corresponding to the color range color_i in this object. We compute a weighted bipartite graph between two histograms as the positive weighted edge represent the bin-wise histogram Distances. Finding a maximum weighted matching of this bipartite graph.
Burglar Identification Through Video Processing II Step II. Collaborative Burglar Identification Through Networked Cameras Burglar Identification: identify the object that has the most occurrences across all videos from those suspicious objects. We formulate the mapping problem as a clique problem First evaluate the likelihood of possible object matches between videos from two cameras based color, height, speed similarity. We then build a similarity graph G = (O,E), where O is the set of objects from all surveillance cameras. Consider two objects from different cameras, we add a edge between them if their similarity is greater than a pre-defined threshold. Finding a maximum clique in this graph.
Evaluation Results • TelosB node [24] as wireless module node. • Canon PowerShot A3300 IS (16 megapixels ) as camera. • The cameras sample the visual information of the surveillance regions at a frame rate of 15 Hz, and the resolution of the captured video sequence is 360×240 pixels. • The video processing algorithm was carried out on the platform of VC++ .NET 2005 combined with OpenCV (the open source computer vision library supported by Intel Corporation).
