A Scalable Architecture for Multi-Class Visual Object Detection

As high-fidelity small form-factor cameras become increasingly available and affordable, there will be a subsequent growth and emergence of vision-based applications that take advantage of this increase in visual information. The key challenge is for the embedded systems, on which the bulk of these applications will be deployed, to maintain real-time performance in the midst of the exponential increase in spatial and temporal visual data. For example, a useful vision-based driver assistance system needs to locate and identify critical objects such as pedestrians, other vehicles, pot-holes, animals, and street signs with latency small enough to allow a human driver to react accordingly. In this work, we propose a digital accelerator architecture for a high-throughput, robust, scalable, and tunable visual object detection pipeline based on Histogram of Oriented Gradients (HOG) features. From a systems perspective, efficacy can be measured in terms of speed, accuracy, energy efficiency and scalability in performing such visual tasks. Since each application dictates the criticality of any one of these dimensions, our proposed architecture exposes design-time parameters that can take advantage of domain-specific knowledge while supporting tune-ability through run-time configurations. To evaluate the effectiveness of our vision accelerator we map the architecture to a modern FPGA and demonstrate full HD video processing at 30 fps (frames per second) operating at a conservative 100 MHz clock. Evaluations on a single object class show throughput improvements of 2x and 5x over GPU and multi-threaded CPU implementations respectively. Further more we provide a pathway for enhanced scalability for the many-class problem and achieve over 20x improvement over an equivalent CPU implementation for 5 object classes.