Embedded Implementation of Human Detection Using Only Color Features on the NVIDIA Xavier The authors are developing a novel vital sensing system for realtime exercises using imageassisted routing (IAR), which requires the accurate detection of humans wearing sensor nodes in aerial images captured by a camera mounted on an UAV for sensor localization. To achieve realtime human detection for IAR, this paper proposes the embedded implementation of an accurate human detection scheme, which is constructed with informed filters using only color features and can achieve higher accuracy than deep learning for sports scenes. The experimental results for the actual aerial images of a sports scene revealed that our implementation on the NVIDIA Jetson Xavier board could process a 3840xc3x972160 imaae in annroximatelv 44.93 ms.

FPGAbased Accurate Pedestrian Detection with Thermal Camera for Surveillance System A surveillance system requires to achieve high accuracy of object detection at all times and to meet realtime processing requirements (30 frames per second) with high energyefficiency. Since thermal cameras allow to see even in darkness unlike a RGB camera, object detection with a thermal camera obtains higher accuracy in the night, and thereby it has attracted much attention. However, since it is challenging to extract informative features from a thermal image, implementation challenges of an object detection with high accuracy remain. To meet the requirements, we present a sparse YOLOv2based pedestrian detector with a thermal camera on an FPGA. For high accuracy, we propose a preprocessing that concatenates a thermal image with the background subtracted one for a detector to extract more informative features. Also, we develop a zero weight skipping architecture dedicated to our detector that contains a vectorizing unit that packs successive valid values into the same memory address to realize high parallel degree calculation. It leads to meet the realtime processing requirement with high energyefficiency. Compared with a conventional one, Fscore was 29 points higher, and speed was 3.3 times faster. Therefore, our system is more suitable for surveillance systems.