Multi-cue object detection and tracking for security in complex environments

Abstract

Efficient moving object tracking requires near flawless detection results to establish correct correspondences between frames. This is especially true in the defense sector where accuracy and speed are critical factors of success. However, problems such as camera motion, lighting and weather changes, texture variation and inter-object occlusions result in misdetections or false positive detections which in turn, lead to broken tracks. In this paper, we propose to use background subtraction and an optimized version of Horn & Schunk's optical flow algorithm in order to boost detection response. We use the frame differencing method, followed by morphological operations to show that it works in many scenarios and the optimized optical flow technique serves to complement the detector results. The Horn & Schunk's method yields color-coded motion vectors for each frame pixel. To segment the moving regions in the frame, we apply color thresholding to distinguish the blobs. Next, we extract appearance-based features from the detected object and establish the correspondences between objects' features, in our case, the object's centroid. We have used the Euclidean distance measure to compute the minimum distances between the centroids. The centroids are matched by using Hungarian algorithm, thus obtaining point correspondences. The Hungarian algorithm's output matrix dictates the objects' associations with each other. We have tested the algorithm to detect people in corridor, mall and field sequences and our early results with an accuracy of 86.4% indicate that this system has the ability to detect and track objects in video sequences robustly.