Vehicle type classification is a critical function in any intelligent transportation system (ITS). In this paper, we present a novel two-layer vehicle type classification framework based on the vehicle's 3D parameters and its local features. This framework is a part of the first Moroccan video intelligent transport system (MOVITS) that aims to control traffic and road code violations. In the first layer, the 3D features are extracted using the disparity map generated from stereo-images, and then, the width, height, and length of the vehicle are calculated based on the obtained list of 3D points. In the second layer, a gradient-based method is applied to extract the 2D features, and a dimensional reduction algorithm is performed to reduce its size. Both features are combined to construct the final feature vector that is used as an input for the classification. The Moroccan dataset and the BIT dataset were used to, respectively, validate the proposed framework and conduct a comparative study with the state-of-the-art algorithms. The experimental results demonstrate the efficiency of our approach against existing algorithms.

We present a new method for object tracking; we use an efficient local search scheme based on the Kalman filter and the probability product kernel (KFPPK) to find the image region with a histogram most similar to the histogram of the tracked target. Experimental results verify the effectiveness of this proposed system.

Moroccan Intelligent Transport System is the first Moroccan system that uses the latest advances in computer vision, machine learning and deep learning techniques to manage Moroccan traffic and road violations. In this paper, we propose a fully automatic approach to Multiple Hypothesis Detection and Tracking (MHDT) for video traffic surveillance. The proposed framework combines Kalman filter and data association-based tracking methods using YOLO detection approach, to robustly track vehicles in complex traffic surveillance scenes. Experimental results demonstrate that the proposed approach is robust to detect and track the trajectory of the vehicles in different situations such as scale variation, stopped vehicles, rotation, varying illumination and occlusion. The proposed approach shows a competitive results (detection: 94.10% accuracy, tracking: 92.50% accuracy ) compared to the state-of-the-art approaches.

Moving object tracking is a tricky job in computer vision problems. In this approach, the object tracking system relies on the deterministic search of target, whose color content matches a reference histogram model. A simple RGB histogrambased color model is used to develop our observation system. Secondly and finally, we describe a new approach for moving object tracking with particle filter by shape information. Particle filtering has been proven very successful for non-gaussian and non-linear estimation problems. In this approach we combine between particle filter and the probability product kernels as a similarity measure using integral image to compute the histograms of all possible target regions of object tracking in video sequence. The shape similarity between a target and estimated regions in the video sequence is measured by their normalized histogram. Target of object tracking is created instantly by selecting an object from the video sequence by a rectangle. Experimental results have been presented to show the effectiveness of our proposed system.

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