The Community for Technology Leaders
Green Image
Issue No. 06 - June (2012 vol. 34)
ISSN: 0162-8828
pp: 1056-1067
D. Damen , Dept. of Comput. Sci., Univ. of Bristol, Bristol, UK
D. Hogg , Sch. of Comput., Univ. of Leeds, Leeds, UK
This paper proposes a method for detecting objects carried by pedestrians, such as backpacks and suitcases, from video sequences. In common with earlier work [14], [16] on the same problem, the method produces a representation of motion and shape (known as a temporal template) that has some immunity to noise in foreground segmentations and phase of the walking cycle. Our key novelty is for carried objects to be revealed by comparing the temporal templates against view-specific exemplars generated offline for unencumbered pedestrians. A likelihood map of protrusions, obtained from this match, is combined in a Markov random field for spatial continuity, from which we obtain a segmentation of carried objects using the MAP solution. We also compare the previously used method of periodicity analysis to distinguish carried objects from other protrusions with using prior probabilities for carried-object locations relative to the silhouette. We have reimplemented the earlier state-of-the-art method [14] and demonstrate a substantial improvement in performance for the new method on the PETS2006 data set. The carried-object detector is also tested on another outdoor data set. Although developed for a specific problem, the method could be applied to the detection of irregularities in appearance for other categories of object that move in a periodic fashion.
video signal processing, image motion analysis, image representation, image segmentation, image sequences, Markov processes, maximum likelihood estimation, object detection, random processes, irregularity detection, carried object detection, walking pedestrian, video sequence, motion representation, shape representation, foreground segmentation, unencumbered pedestrian, likelihood map, Markov random field, carried object segmentation, MAP solution, Legged locomotion, Trajectory, Correlation, Cameras, Noise, Detectors, Lattices, periodicity analysis., Baggage detection, carried-objects detection, silhouette analysis, temporal templates, template matching

D. Damen and D. Hogg, "Detecting Carried Objects from Sequences of Walking Pedestrians," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1056-1067, 2012.
91 ms
(Ver 3.3 (11022016))