STRAY DOG DETECTION IN WIRED CAMERA NETWORK

Existing surveillance systems impose high level of security on humans but lacks attention on animals. Stray dogs could be used as an alternative to humans to carry explosive material. It is therefore imperative to ensure the detection of stray dogs for necessary corrective action. In this paper, a novel composite approach to detect the presence of stray dogs is proposed. The captured frame from the surveillance camera is initially pre-processed using Gaussian filter to remove noise. The foreground object of interest is extracted utilizing ViBe algorithm. Histogram of Oriented Gradients (HOG) algorithm is used as the shape descriptor which derives the shape and size information of the extracted foreground object. Finally, stray dogs are classified from humans using a polynomial Support Vector Machine (SVM) of order 3. The proposed composite approach is simulated in MATLAB and OpenCV. Further it is validated with real time video feeds taken from an existing surveillance system. From the results obtained, it is found that a classification accuracy of about 96% is achieved. This encourages the utilization of the proposed composite algorithm in real time surveillance systems.