This paper presents a novel algorithm for handling occlusion in visual traffic surveillance (VTS) by geometrically splitting the model that has been fitted onto the composite binary vehicle mask of two occluded vehicles. The proposed algorithm consists of a critical points detection step, a critical points clustering step and a model partition step using the vanishing point of the road. The critical points detection step detects the major critical points on the contour of the binary vehicle mask. The critical points clustering step selects the best critical points among the detected critical points as the reference points for the model partition. The model partition step partitions the model by exploiting the information of the vanishing point of the road and the selected critical points. The proposed algorithm was tested on a number of real traffic image sequences, and has demonstrated that it can successfully partition the model that has been fitted onto two occluded vehicles. To evaluate the accuracy, the dimensions of each individual vehicle are estimated based on the partitioned model. The estimation accuracies in vehicle width, length and height are 95.5%, 93.4% and 97.7% respectively.
In modern traffic surveillance, computer vision methods are often employed to detect vehicles of interest because of the rich information content contained in an image. In this paper, we propose an efficient method for extracting the boundary of vehicles free from their moving cast shadows and reflective regions. The extraction method is based on the hypothesis that regions of similar texture are less discriminative, disregarding intensity differences between the vehicle body and the cast shadow or reflection on the vehicle. In this novel algorithm, a united likelihood map that based on the relationship of texture, luminance and chrominance of each pixel is initially constructed. Subsequently, a foreground mask is constructed by applying morphological operations. Vehicles can be successfully extracted and different vehicle components can be efficiently distinguished by the related autocorrelation index within the vehicle mask.