Motion detection, object classification and tracking for
visual surveillance application

Panda, Deepak Kumar (2012) Motion detection, object classification and tracking for
visual surveillance application.
MTech thesis.



Visual surveillance in dynamic scenes, especially for humans and vehicles, is one of the current challenging research topics in computer vision. It is a key technology to fight against terrorism, crime, public safety and for efficient management of traffic. The work involves designing of efficient visual surveillance system in complex environments. In video surveillance, detection of moving objects from a video is important for object classification, target tracking, activity recognition, and behavior understanding. Detection of moving objects in video streams is the first relevant step of information and background subtraction is a very popular approach for foreground segmentation. In this thesis, we have simulated different background subtraction methods to overcome the problem of illumination variation, background clutter, shadows, and camouflage. Object classification is done using silhouette template based classification to categorize objects into human, group of human and vehicle. Detecting and tracking of human body parts is important in understanding human activities. We have proposed two methods to overcome the problem of object tracking in varying illumination condition and background clutter. For target tracking of interested object in the consecutive video frames, we have used normalized correlation coefficient (NCC). NCC is robust to varying illumination condition. Template is updated on every frame to minimize the template drift problem and it also tries to cope with short-lived occlusion and background clutter. In order to extend the surveillance area and overcome occlusion, fusion of data from multiple cameras is employed in our project. We have tracked objects across multiple cameras with non-overlapping FOVs based on object appearances. A brightness transfer function (BTF) is determined from the cumulative histograms of the images. Matching of the object is done, with the help of Bhattacharya distance.

Item Type:Thesis (MTech)
Uncontrolled Keywords:Motion detection, Background subtraction, Object classification, Object tracking
Subjects:Engineering and Technology > Electronics and Communication Engineering > Image Processing
Divisions: Engineering and Technology > Department of Electronics and Communication Engineering
ID Code:4114
Deposited By:Mr. Deepak Kumar Panda
Deposited On:13 Jun 2012 14:23
Last Modified:13 Jun 2012 14:23
Supervisor(s):Meher, S

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