Instance Segmentation and Detection of Children to Safeguard Vulnerable Traffic User by Infrastructure

Shiva Agrawal, Savankumar Bhanderi, Sumit Amanagi, Kristina Doycheva, Gordon Elger

Abstract: Cameras mounted on intelligent roadside infrastructure units and vehicles can detect humans on the road using state-of-the-art perception algorithms, but these algorithms are presently not trained to distinguish between human and adult. However, this is a crucial requirement from a safety perspective because a child may not follow all the traffic rules, particularly while crossing the road. Moreover, a child may stop or may start playing on the road. In such situations, the separation of a child from an adult is necessary. The work in this paper targets to solve this problem by applying a transfer-learning-based neural network approach to classify child and adult separately in camera images. The described work is comprised of image data collection, data annotation, transfer learning-based model development, and evaluation. For the work, Mask-RCNN (region-based convolutional neural network) with different backbone architectures and two different baselines are investigated and the perc

External IDs:dblp:conf/vehits/AgrawalBADE23