Object tracking based on sparse representation of gradient feature

As traditional compressive sensing tracking algorithm will produce tracking errors in circumastances when illumination has dramatic change or there exists a object similar to the target in background, this paper proposes a sparse representation object tracking algorithm by taking the histogram of gradient feature to replace the generalized Haar feature. The algorithm uses the histogram of gradient feature as an original feature firstly, and gets the sparse representation of object feature subspace by using compressive sensing theory. In the subsequent frames, the naive Bayes classifier is used to search the target location and the classifier is online updated finally. As the histogram of gradient feature can represent the target more stably, this algorithm is more robust than original compressive tracking algorithm. Furthermore, the integral histogram is adapted to effectively reduce computational load when the gradient feature is computed. Experiments on different videos show that the tracking algorithm can reach the tracking rate of 10 frames per second in an experimental environment of Intel Core2 2. 93 GHz, matlab R2010a, image size 320×240, and it achieves stable tracking in some special conditions as mentioned above.