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Occlusion Robust and Illumination Invariant Vehicle Tracking for Acquiring Detailed Statistics from Traffic Images
Shunsuke KAMIJO Tsunetoshi NISHIDA Masao SAKAUCHI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2002/11/01
Print ISSN: 0916-8532
Type of Manuscript: Special Section PAPER (Special Issue on Information System Technologies for ITS)
Spatio-Temporal MRF model, tracking, occlusion, illumination invariant, traffic statistics,
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Among ITS applications, it is very important to acquire detailed statistics of traffic flows. For that purpose, vision sensors have an advantage because of their rich information compared to such spot sensors such as loop detectors or supersonic wave sensors. However, for many years, vehicle tracking in traffic images has suffered from the problems of occlusion effect and illumination effect. In order to resolve occlusion problems, we have been proposing the Spatio-Temporal Markov Random Field model(S-T MRF) for segmentation of Spatio-Temporal images. This S-T MRF model optimizes the segmentation boundaries of occluded vehicles and their motion vectors simultaneously by referring to textures and segment labeling correlations along the temporal axis as well as the spatial axis. Consequently, S-T MRF has been proven to be successful for vehicle tracking even against severe occlusions found in low-angle traffic images with complicated motions, such at highway junctions. In addition, in this paper, we define a method for obtaining illumination-invariant images by estimating MRF energy among neighbor pixel intensities. These illumination-invariant images are very stable even when sudden variations in illumination or shading effect are occurred in the original images. We then succeeded in seamlessly integrating the method for MRF energy images into our S-T MRF model. Thus, vehicle tracking was performed successfully by S-T MRF, even against sudden variations in illumination and against shading effects . Finally, in order to verify the effectiveness of our tracking algorithm based on the S-T MRF for practical uses, we developed an automated system for acquiring traffic statistics out of a flow of traffic images. This system has been operating continuously for ten months, and thus effectiveness of the tracking algorithm based on S-T MRF model was proven.