Improved segmentation of occluded and adjoining vehicles in traffic surveillance videos

Medha Juneja; Priyanka Grover

doi:10.1117/12.2050066

24 December 2013 Improved segmentation of occluded and adjoining vehicles in traffic surveillance videos

Medha Juneja, Priyanka Grover

Proceedings Volume 9067, Sixth International Conference on Machine Vision (ICMV 2013); 90670M (2013) https://doi.org/10.1117/12.2050066
Event: Sixth International Conference on Machine Vision (ICMV 13), 2013, London, United Kingdom

Abstract

Occlusion in image processing refers to concealment of any part of the object or the whole object from view of an observer. Real time videos captured by static cameras on roads often encounter overlapping and hence, occlusion of vehicles. Occlusion in traffic surveillance videos usually occurs when an object which is being tracked is hidden by another object. This makes it difficult for the object detection algorithms to distinguish all the vehicles efficiently. Also morphological operations tend to join the close proximity vehicles resulting in formation of a single bounding box around more than one vehicle. Such problems lead to errors in further video processing, like counting of vehicles in a video. The proposed system brings forward efficient moving object detection and tracking approach to reduce such errors. The paper uses successive frame subtraction technique for detection of moving objects. Further, this paper implements the watershed algorithm to segment the overlapped and adjoining vehicles. The segmentation results have been improved by the use of noise and morphological operations.

Citation Download Citation

Medha Juneja and Priyanka Grover "Improved segmentation of occluded and adjoining vehicles in traffic surveillance videos", Proc. SPIE 9067, Sixth International Conference on Machine Vision (ICMV 2013), 90670M (24 December 2013); https://doi.org/10.1117/12.2050066

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KEYWORDS

Video

Video surveillance

Image segmentation

Surveillance

Detection and tracking algorithms

Video processing

Cameras

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