Authors:
Philipp Wojaczek
1
;
Marcus Laumer
2
;
Peter Amon
3
;
Andreas Hutter
3
and
André Kaup
1
Affiliations:
1
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Germany
;
2
Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and Siemens Corporate Technology, Germany
;
3
Siemens Corporate Technology, Germany
Keyword(s):
Object Detection, Person Detection, Tracking, Compressed Domain, Pixel Domain, H.264/AVC, Macroblocks, Compression, Color Histogram, Hue, HSV, Segmentation.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
;
Image and Video Coding and Compression
;
Image Formation and Preprocessing
;
Motion, Tracking and Stereo Vision
;
Optical Flow and Motion Analyses
;
Segmentation and Grouping
;
Video Surveillance and Event Detection
Abstract:
In this paper we present a new hybrid framework for detecting and tracking persons in surveillance video streams compressed according to the H.264/AVC video coding standard. The framework consists of three stages and operates in both the compressed and the pixel domain of the video. The combination of compressed and pixel domain represents the hybrid character. Its main objective is to significantly reduce the amount of computation required, in particular for frames and image regions with few people present. In its first stage the proposed framework evaluates the header information for each compressed frame in the video sequence, namely the macroblock type information. This results in a coarse binary mask segmenting the frame into foreground and background. Only the foreground regions are processed further in the second stage that searches for persons in the image pixel domain by applying a person detector based on the Implicit Shape
Model. The third stage segments each detected pers
on further with a newly developed method that fuses information from the first two stages. This helps obtaining a finer segmentation for calculating a color histogram suitable for tracking the person using the mean shift algorithm. The proposed framework was experimentally evaluated on a publicly available test set. The results demonstrate that the proposed framework reliably separates frames with and without persons such that the computational load is significantly reduced while the detection performance is kept.
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