Faculty Publications
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Item A Comparative Study of Illumination Invariant Techniques in Video Tracking Perspective(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2020) Asha, C.S.; Narasimhadhan, A.V.Object tracking is being utilized in the field of computer vision over decades for video surveillance, human–computer interaction and robotic applications. Even though the state-of-the-art tracking technology is rapidly growing, few issues are still challenging such as illumination variation, pose variation, scale changes, occlusion, etc. Among these challenges, sudden illumination variation is more complicated which is not solved completely. Most of the current trackers, indeed work under controlled illumination conditions in outdoor and indoor environments. In this work, we study the effect of adding the photometric normalization techniques prior to tracking in order to minimize the drift during abrupt light changes of the median flow tracker (MFT). The tracker under investigation is based on the optical flow method and achieved remarkable results in the tracking literature. However, it drifts off during sudden illumination variation. To resolve this problem, pre-processing technique is incorporated just before tracking. Hence, we present an experimental study of various pre-processing techniques to improve the accuracy of the MFT. A total of eight state-of-the-art normalization techniques are summarized and tested in video tracking perspective. The experiments are carried out with the video sequences obtained from the object tracking benchmark dataset posing sudden illumination change as a challenge to analyze the modified tracker. A comparative analysis indicates that the modified tracker outperforms the baseline tracker in terms of precision score and overlap score. © 2019 IETE.Item Visual Tracking Using Kernelized Correlation Filter with Conditional Switching to Median Flow Tracker(Taylor and Francis Ltd, 2020) Asha, C.S.; Narasimhadhan, A.V.The correlation filters (CF) have been extensively used in object tracking due to its robustness and attractive computational speed. However, the CF are more sensitive to object deformation because they are trained using the spatial features. Besides, updating the filter template with slightly drifted or occluded samples increase the probability of tracking failure. In contrast, the median flow tracker is complementary to the correlation techniques and is fast, robust to occlusion and deformation, but sensitive to illumination variation. In this paper, we exploit the advantage of correlation and optical flow based trackers to achieve drift free tracking. Hence, we apply the CF-based tracker to track an object and switch to the modified median flow tracker during the drift conditions. The combined model is optimized to cope up with the fast appearance change and recover from drifting. We also propose an adaptive feature selection process to select the most discriminative feature/features among colour name and histogram of oriented gradient features based on object separation from the background in intensity and colour channels. The proposed tracker updates the filter template dynamically, depending on the appearance of an object using an adaptive learning rate to track the object irrespective of occlusion, motion blur, and deformation. The scale of object is estimated using Lucas-Kanade homography method. The experiments are carried out using challenging video sequences from a standard object tracking benchmark dataset and show the best performance among the state-of-the-art techniques. © 2020, © 2020 IETE.