This paper presents a visual-based dimension estimation method for vehicle type classification. Our method extracts moving vehicles from traffic image sequences and fits them with a simple deformable vehicle model. Using a set of coordination mapping functions derived from a calibrated camera model and relying on a shadow removal method, vehicle's width, length and height are estimated. Our experimental tests show that the modeling method is effective and the estimation accuracy is sufficient for general vehicle type classification.
This paper presents a stationary background estimation algorithm for color image sequence. The algorithm employs the running mode and running average algorithms, which are two commonly used algorithms, as the estimation core. A scoreboard is used to kept the pixel variations in the image sequence and is used to select between the running mode or the running average algorithm in each estimation. Our evaluation results show that by selecting, intelligently, the estimation core between the two algorithms according to the scoreboard values, the proposed background estimation algorithm has excellent performance in terms of estimation accuracy and speed.
This paper presents a method of automated virtual loop assignment and direction-based motion estimation. The unique features of our approach are that first, a number of loops are automatically assigned to each lane. The merit of doing this is that it accommodates pan-tilt-zoom (PTZ) actions without needing further human interaction. Second, the size of the virtual loops is much smaller for estimation accuracy. This enables the use of standard block-based motion estimation techniques that are well developed for video coding. Third, the number of virtual loops per lane is large. The motion content of each block may be weighted and the collective result offers a more reliable and robust approach in motion estimation. Comparing this with traditional inductive loop detectors (ILDs), there are a number of advantages. First, the size and number of virtual loops may be varied to fine-tune detection accuracy. Second, it may also be varied for an effective utilization of the computing resources. Third, there is no failure rate associated with the virtual loops or physical installation. As the loops are defined on the image sequence, changing the detection configuration or redeploying the loops to other locations on the same image sequence requires only a change of the assignment parameters. Fourth, virtual loops may be reallocated anywhere on the frame, giving flexibility in detecting different parameters. Our simulation results indicate that the proposed method is effective in type classification.
Detect occlusionDetermine the 3D parameters --+ of the model Match by the generalized ' deformable model This paper presents a vehicle occlusion detection algorithm based on a generalized deformable model. A 3D solid cuboid model with up to six vertices is employed to fit any vehicle images, by varying the vertices for a best fit. The advantage of using such a model is that the number of parameterized vertices is small which can be easily deformed. Occlusion is detected by recording the changes in the Area Ratio and the dimensions of the generalized deformable model. Our tests show that the new modeling algorithm is effective in detecting vehicle occlusion 1.
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