Pulse-Doppler radars require high-computing power. A massively parallel machine has been developed in this paper to implement a Pulse-Doppler radar signal processing chain in real-time fashion. The proposed machine consists of two C6678 digital signal processors (DSPs), each with eight DSP cores, interconnected with Serial RapidIO (SRIO) bus. In this study, each individual core is considered as the basic processing element; hence, the proposed parallel machine contains 16 processing elements. A straightforward model has been adopted to distribute the Pulse-Doppler radar signal processing chain. This model provides low latency, but communication inefficiency limits system performance. This paper proposes several optimizations that greatly reduce the inter-processor communication in a straightforward model and improves the parallel efficiency of the system. A use case of the Pulse-Doppler radar signal processing chain has been used to illustrate and validate the concept of the proposed mapping model. Experimental results show that the parallel efficiency of the proposed parallel machine is about 90%.

This work is part of developing a new type of radars which is based on stereoscopic effect obtained by using two cameras. The main work is to develop an algorithm for speed estimation. We begin by detecting motions and tracking vehicles in order to identify the vehicle in the next frame. Stereoscopic pictures allow us to calculate the distance from the cameras to the chosen object within the picture. The distance is calculated from differences between the pictures and by using intrinsic and extrinsic cameras' parameters. The object is selected on the left picture, while the same object on the right picture is automatically detected by calculating the cross-correlation's score between both pictures. The object's position can be calculated by doing some geometrical derivations. The speed is estimated by calculating the slope of the distances estimated in several frames. The accuracy of the position depends on picture resolution, optical distortions and distance between the cameras.

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.

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