Benchmarking Video Object Detection Systems on Embedded Devices under Resource Contention

Adaptive and efficient computer vision systems have been proposed to make computer vision tasks, e.g., object classification and object detection, optimized for embedded boards or mobile devices. These studies focus on optimizing the model (deep network) or system itself, by designing an efficient network architecture or adapting the network architecture at runtime using approximation knobs, such as image size, type of object tracker, head of the object detector (e.g., lighter-weight heads such as one-shot object detectors like YOLO over two-shot object detectors like FRCNN). In this work, we benchmark different video object detection protocols, including FastAdapt, with respect to accuracy, latency, and energy consumption on three different embedded boards that represent the leading edge mobile GPUs. Our set of protocols consists of Faster R-CNN, YOLOv3, SELSA, MEGA, and REPP. Further, we characterize their performance under different levels of resource contention, specifically GPU contention, as would arise due to co-located applications on these boards, contending with the video object detection task. Our key insights are that object detectors have to be coupled with trackers to keep up with the latency requirements (e.g., 30 fps). With this, FastAdapt achieves up to 76 fps on the most well-resourced NVIDIA Jetson-class board---the NVIDIA AGX Xavier. Second, adaptive protocols like FastAdapt, FRCNN, and YOLO (specifically our adaptive variants, FRCNN+ and YOLO+) work well under resource constraints. Among the latest video object detection heads, SELSA achieves the highest accuracy but at a latency of over 2 sec per frame. Our energy consumption experiments bring out that FastAdapt, adaptive FRCNN, and adaptive YOLO are best-in-class, relative to the non-adaptive protocols SELSA, MEGA, and REPP.