Object detection by spatio-temporal analysis and tracking of the detected objects in a video with variable background

In this paper we propose a novel approach for detecting and tracking objects in videos captured by moving cameras without any additional sensor. In such a video both the background and foreground change in each frame of the image sequence; making the separation of actual moving object from the background a challenging task. In this work, moving objects are detected as clusters of spatio-temporal blobs generated by spatio-temporal analysis of the image sequence using a three-dimensional Gabor filter and merged using Minimum Spanning Tree. Problem of data association during tracking is solved by Linear Assignment Problem and occlusion is handled by the application of Kalman filter. The major advantage of the proposed method is that, it does not require initialization or training on sample data to perform. Our algorithm demonstrated very satisfactory state-of-the-art result on benchmark videos. The performance of the algorithm is equivalent or superior to some benchmark algorithms. (C) 2018 Published by Elsevier Inc.