Visual object tracking using co-difference features
Embargo Lift Date: 2020-08-08
Item Usage Stats
Visual object tracking has been one of the widely studied computer vision tasks which has a broad range of applications in various areas from surveillance to medical studies. There are different approaches proposed for the problem in the literature. While some of them use generative methods where an appearance model is built and used for localizing the object on the image, others use discriminative approaches that models the object and background as two different classes and turns the tracking task into a binary classification problem. In this study, we propose a novel object tracking algorithm based on co-difference matrix and compare its performance with the recent state-of-the-art tracking algorithms on two specific applications. Experiments on a large class of datasets show that the proposed co-difference based object tracking algorithm has successful results in terms of track maintenance, success rate and localization accuracy. The proposed algorithm uses co-difference matrix as the image descriptor. Extraction of co-difference features is similar to the well known covariance method. However the vector product operator is redefined in a multiplication-free manner. The new operator yields a computationally efficient implementation for real time object tracking applications. For our experiments, we prepared a comparison framework that contains over 70000 annotated images for visual object tracking task. We conducted experiments for two different application areas seperately. The first one is infrared surveillance sytems. For this application, we used a thermal image dataset that contains various objects such as humans, cars and military vehicles. The second application area is cell tracking on time-lapse microscopy images. Image sequences for the second application contain cells of different shapes and sizes. For both applications, datasets include a considerable amount of rotation and background clutter. Performance of the tracking algorithms are evaluated quantitatively based on three different metrics. These metrics measure the track maintenance score, success rate and localization accuracy of an algorithm. Experiments indicate that the proposed co-difference based tracking algorithm is among the best performing methods by having the highest localization accuracy and success rate for the surveillance dataset, and the highest track maintenance score for the cell motility dataset.