  Recent advancements in parallel computing, GPU technology and deep learning
provide a new platform for complex image processing tasks such as person
detection to flourish. Person detection is fundamental preliminary operation
for several high level computer vision tasks. One industry that can
significantly benefit from person detection is retail. In recent years, various
studies attempt to find an optimal solution for person detection using neural
networks and deep learning. This study conducts a comparison among the state of
the art deep learning base object detector with the focus on person detection
performance in indoor environments. Performance of various implementations of
YOLO, SSD, RCNN, R-FCN and SqueezeDet have been assessed using our in-house
proprietary dataset which consists of over 10 thousands indoor images captured
form shopping malls, retails and stores. Experimental results indicate that,
Tiny YOLO-416 and SSD (VGG-300) are the fastest and Faster-RCNN (Inception
ResNet-v2) and R-FCN (ResNet-101) are the most accurate detectors investigated
in this study. Further analysis shows that YOLO v3-416 delivers relatively
accurate result in a reasonable amount of time, which makes it an ideal model
for person detection in embedded platforms.
