Towards Pedestrian Detection Using RetinaNet in ECCV 2018 Wider Pedestrian Detection Challenge

TL;DR

This paper evaluates RetinaNet for pedestrian detection in the ECCV 2018 Wider Pedestrian Detection Challenge, achieving a mAP of 0.4061 and demonstrating its effectiveness for practical applications.

Contribution

The paper introduces a RetinaNet-based pedestrian detection system tailored for the ECCV 2018 challenge, showcasing its competitive performance.

Findings

Achieved 0.4061 mAP on the challenge dataset

Demonstrated RetinaNet's applicability to pedestrian detection

Provided code for reproducibility

Abstract

The main essence of this paper is to investigate the performance of RetinaNet based object detectors on pedestrian detection. Pedestrian detection is an important research topic as it provides a baseline for general object detection and has a great number of practical applications like autonomous car, robotics and Security camera. Though extensive research has made huge progress in pedestrian detection, there are still many issues and open for more research and improvement. Recent deep learning based methods have shown state-of-the-art performance in computer vision tasks such as image classification, object detection, and segmentation. Wider pedestrian detection challenge aims at finding improve solutions for pedestrian detection problem. In this paper, We propose a pedestrian detection system based on RetinaNet. Our solution has scored 0.4061 mAP. The code is available at https://github.com/miltonbd/ECCV_2018_pedestrian_detection_challenege.