Visual Multiple Object Tracking with Improved Feature Detection and Kinect Color Depth Map

Abstract

Tracking multiple objects in real-time videos represents a challenging area in the era of computer vision. This paper proposes a new method to track the multiple objects under different environment conditions such as rotation, illumination, blurred, occlusion, and many others. In addition, the kinect color depth image processing is used to estimate the distance of the objects. The tracking of multiple objects is formulated as classification task which competitively use the object features in the different video frames of the video sequences. To obtain the optimal configuration of feature classification, a neural network based framework is presented to make a global influence based on winner pixel estimation between the video frames. The objects are tracked efficiently in less time as compared with SIFT techniques and distance of objects is calculated with kinect based depth image processing. Experimental results are given for real-time scenes, and many experiments are conducted to examine the performance of the proposed approach. The proposed method resulted into efficient tracking of multiple objects in various conditions including rotation, scaling, occlusion, etc. The distance of multiple tracked objects is estimated using the kinect depth processing.