Object tracking in surveillance videos using compressed domain features from scalable bit-streams

Mehmood, Khizer; Mrak, Marta; Calic, Janko; Kondoz, A.M.

doi:10.1016/j.image.2009.06.006

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…However, the moving objects should neither be too numerous nor should they occupy the whole viewable image area. Mehmood et al [47] came up with the implementation that was not specific to any object or environment as they used only MBs that were entirely inside the target to predict and track the object. Khatoonabadi et al [40] used ST-MRF model to improve the robustness of the algorithm.…”

Section: Discussionmentioning

confidence: 99%

“…This algorithm can deal with moving camera scenarios also. [94] and II Clustering of non-skip MBs [95] Mehmood et al [47] came up with real-time object tracking using motion information in H.264/AVC compressed domain and its SVC extension. First a frame was decoded to select the object to be tracked by defining a model using shape and coordinates.…”

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

“…The magnitude of PVM is the median of magnitudes of all the representative vectors while the orientation is the median of angles of selected representative Fig. 8 Adaptive smoothing for noisy motion vectors a before processing and b after processing [47] vectors. The ST-MRF model helps to optimize object tracking by referring to motion coherence and spatial compactness, as well as temporal continuity of the object's motion.…”

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

See 2 more Smart Citations

A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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Image and video analysis requires rich features that can characterize various aspects of visual information. These rich features are typically extracted from the pixel values of the images and videos, which require huge amount of computation and seldom useful for real-time analysis. On the contrary, the compressed domain analysis offers relevant information pertaining to the visual content in the form of transform coefficients, motion vectors, quantization steps, coded block patterns with minimal computational burden. The quantum of work done in compressed domain is relatively much less compared to pixel domain. This paper aims to survey various video analysis efforts published during the last decade across the spectrum of video compression standards. In this survey, we have included only the analysis part, excluding the processing aspect of compressed domain. This analysis spans through various computer vision applications such as moving object segmentation, human action recognition, indexing, retrieval, face detection, video classification and object tracking in compressed videos.

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Section: Discussionmentioning

confidence: 99%

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

Section: H264/avc (Mpeg-4 Part 10)mentioning

confidence: 99%

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A survey on compressed domain video analysis techniques

Babu

Tom

Wadekar

2014

Multimed Tools Appl

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“…This approach can compete with dynamic camera environments. Real-time object tracking approach was developed by Mehmood et al [22] in H.264. From the video sequence, MVs were fetched to keep informed the tracking model.…”

Section: Background Studies On Object Tracking In Mpeg-4 Part 10 (H264)mentioning

confidence: 99%

Research Inferences In Video Compression Domain

Nihal*¹

2019

IJEAT

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Video Compression (VC) is a painstaking investigation area in the modernization of digital existence because of more evolution being emerged in web relevance. In this direction, video compression is grown-up rapdily and confirmed by the large number of myriad applications in Video streaming, Computer Vision like Video Tampering Detection, Video Surveillance and Camera Moving etc., which collectively use of this compression technology. This paper attributes / opensup / endeavours the whole possible and recent research directions and its challenges by focusing the potential investigations on compression domain of MPEG, H.264, H.265 compression coding standard with latest computing techniques in learning videos via Machine Learning.

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“…Visual object tracking [1][2][3] has been employed in various fields such as video surveillance [4,5], driver assistance systems [6,7], and robotics [8]. Optical flow (OF) [9][10][11] is one of the most widely used visual tracking methods.…”

Section: Introductionmentioning

confidence: 99%