Improving target detection by coupling it with tracking

Target detection and tracking represent two fundamental steps in automatic video-based surveillance systems where the goal is to provide intelligent recognition capabilities by analyzing target behavior. This paper presents a framework for video-based surveillance where target detection is integrated with tracking to improve detection results. In contrast to methods that apply target detection and tracking sequentially and independently from each other, we feed the results of tracking back to the detection stage in order to adaptively optimize the detection threshold and improve system robustness. First, the initial target locations are extracted using background subtraction. To model the background, we employ Support Vector Regression (SVR) which is updated over time using an on-line learning scheme. Target detection is performed by thresholding the outputs of the SVR model. Tracking uses shape projection histograms to iteratively localize the targets and improve the confidence level of detection. For verification, additional information based on size, color and motion information is utilized. Feeding back the results of tracking to the detection stage restricts the range of detection threshold values, suppresses false alarms due to noise, and allows to continuously detect small targets as well as targets undergoing perspective projection distortions. We have validated the proposed framework in two different application scenarios, one detecting vehicles at a traffic intersection using visible video and the other detecting pedestrians at a university campus walkway using thermal video. Our experimental results and comparisons with frame-based detection and kernel-based tracking methods illustrate the robustness of our approach.