Showing papers by "Malay K. Kundu published in 2018"
TL;DR: It is observed that the proposed IT2FS based threshold technique can achieve a higher segmentation accuracy in comparison to other state-of-the-art methods when they are benchmarked against the Modified Cramer–Rao Bound.
36 citations
TL;DR: A unified framework for the simultaneous detection of both AT and GT have been proposed in this article, which uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames.
Abstract: The fundamental step in video content analysis is the temporal segmentation of video stream into shots, which is known as Shot Boundary Detection (SBD). The sudden transition from one shot to another is known as Abrupt Transition (AT), whereas if the transition occurs over several frames, it is called Gradual Transition (GT). A unified framework for the simultaneous detection of both AT and GT have been proposed in this article. The proposed method uses the multiscale geometric analysis of Non-Subsampled Contourlet Transform (NSCT) for feature extraction from the video frames. The dimension of the feature vectors generated using NSCT is reduced through principal component analysis to simultaneously achieve computational efficiency and performance improvement. Finally, cost efficient Least Squares Support Vector Machine (LS-SVM) classifier is used to classify the frames of a given video sequence based on the feature vectors into No-Transition (NT), AT and GT classes. A novel efficient method of training set generation is also proposed which not only reduces the training time but also improves the performance. The performance of the proposed technique is compared with several state-of-the-art SBD methods on TRECVID 2007 and TRECVID 2001 test data. The empirical results show the effectiveness of the proposed algorithm.
28 citations
TL;DR: Enhanced features of the fundamental coding unit, i.e., the macroblock (MB), are proposed, based on the temporal statistics of the MB features gathered from initial frames, and MBs that potentially contain parts of moving objects are selected in subsequent frames.
Abstract: While H.264 is a well-established standard for video surveillance, its high-profile implementation, in particular, has the unique capabilities that pack more visual detail into a given bitrate. Several algorithms exist that detect moving objects from the H.264 bitstream, most of which end up incorrectly classifying the nonstationary or dynamic components (e.g., jitter camera, waving trees, ripples, etc.) of the background as foreground. Moreover, the coarse quantization schemes adopted at constrained bitrates pose new challenges for direct identification of moving objects/targets from the bitstream. This paper focuses on dynamic background modeling for the H.264 video encoded at very low bitrates. To this end, enhanced features of the fundamental coding unit, i.e., the macroblock (MB) are proposed. Based on the temporal statistics of the MB features gathered from initial frames, MBs that potentially contain parts of moving objects are selected in subsequent frames. The selected MBs constitute a coarse segmentation map of the foreground at the MB level. Finally, pixel-level segmentation of the foreground is obtained by comparing pixels constituting the selected MBs with the colocated counterparts of a background frame. Experimental results showing the comparison of bitstreams encoded at strikingly low bitrates are obtained over a diverse set of standardized surveillance sequences.
9 citations