A robust real-time object detection and tracking system

We propose a real-time vehicle detection and tracking system from an electro-optical (EO) surveillance camera. Real time object detection remains a challenging computer vision problem in uncontrolled environments. The state-of-the-art adaboosting technique is used to serve as a robust object detector. In addition to the generally-used Haar features, we propose to include corner features to improve the detection performance of the vehicles. Having the objects of interest detected, we use the detection results to initialize the object tracking module. We propose an advanced, adaptive particle-filtering based algorithm to robustly track multiple mobile targets by adaptively changing the appearance model of the selected targets. We use the affine transformation to describe the motion of the object across frames. By drawing multiple particles on the transformation parameters, our approach provides high performance while facilitating implementation of this algorithm in hardware with parallel processing. In order to resume from the lost track case, which may result from the objects' out of boundary or being occluded, we utilize the prior information (height-to-width ratio) and the temporal information of the objects to estimate if the tracking is reliable. Object detectors will be evoked at the frames which fail in tracking the objects reliably. We also check for occlusion by comparing hue values within the rectangular region for the current frame with that of the previous frame. Detection is re-initialized for the next frame if an occlusion is claimed for the current frame. The system works very well in terms of speed and performance for the real surveillance video.