Accelerated and optimized covariance descriptor for pedestrian detection in self-driving cars

Self-Driving vehicles are expected to thrive in the coming years. These vehicles are designed to analyze the environment around them in real-time to identify obstacles and hazards. One of the most important aspects of designing a self-driving vehicle is to preserve the safety of pedestrians. This requires accurate and rapid pedestrian detection, which is a key operation in various other applications including video surveillance and assisted living. The covariance descriptor is one of the most efficient descriptors used in detecting pedestrians. However, the descriptor is compute-intensive; rendering it less favorable for real-time applications. This paper proposes an accelerated and optimized implementation of the descriptor. Instead of mapping the entire descriptor to a hardware accelerator, we opt for a heterogeneous architecture. In particular, compute-intensive components of the descriptor are accelerated on hardware, while the other components are executed on an embedded processor. The proposed architecture combines both speed and flexibility while being watchful of precious hardware resources. This architecture was validated on a Zynq SoC platform, which hosts FPGA fabric along with an ARM processor. The results of executing the descriptor on the platforms show a performance gain of up to 13.52 × when compared to pure software implementation of the descriptor.