|M.Sc Student||Geva Amir|
|Subject||Far Field Surveillance Target Classification|
|Department||Department of Computer Science||Supervisor||Professor Michael Heymann|
|Full Thesis text|
Automated processing of surveillance video is a growing field. Classification of targets in surveillance videos is of great importance for efficient security. This thesis report summarizes research on the problem of classification of far field targets in surveillance videos. Far field targets are too far from the camera, and thus too small for standard classification methods. Classification of very small targets requires the utilization of all information available in the video sequence. The research covered various parts involved in the construction of a robust and accurate classifier. It is assumed that the classifier is a part of a larger system that includes object detection, tracking, and segmentation which are not in scope of this research. The report describes novelties in the steps of: feature extraction, covering static shape features and also introducing motion based features; feature selection, describing a genetic algorithm wrapper method to select the optimal subset of features; sequence classification which takes information from a sequence of multiple frames in order to make a decision that is impossible based on a single frame using a method of error correction output codes and delayed voting; and occlusion handling, which filters out instances of the target that are not fully visible. The features selected bring a simple voting classifier to a level of 91.4% accuracy, whereas the novelties that take advantage of the sequences and filter out noise, as described in our research, show an improvement that brings the resulting classifier to an accuracy level of over 95.7% on the test benchmark.