The AI City Challenge, hosted at CVPR 2024, focuses on harnessing AI to enhance operational efficiency in physical settings such as retail and warehouse environments, and Intelligent Traffic Systems (ITS). It aims to utilize AI for actionable insights from sensor data, like camera feeds, to improve traffic safety and transportation outcomes. This year, the challenge spotlights two key areas with significant potential: retail business and ITS.

Key areas of interest include multi-camera people tracking, traffic safety analysis, naturalistic driving action recognition, fish-eye camera road object detection, and motorcycle helmet rule violation detection. We invite original contributions in these domains, leveraging computer vision, natural language processing, and deep learning for practical, large-scale applications that enhance safety and intelligence in our environments.

Fisheye lenses have gained popularity owing to their natural, wide, and omnidirectional coverage, which traditional cameras with narrow fields of view (FoV) cannot achieve. In traffic monitoring systems, fisheye cameras are advantageous as they effectively reduce the number of cameras required to cover broader views of streets and intersections. Despite these benefits, fisheye cameras present distorted views that necessitate a non-trivial design for image undistortion and unwarping or a dedicated design for handling distortions during processing. It is worth noting that, to the best of our knowledge, there is no open dataset available for fisheye road object detection for traffic surveillance applications. The datasets (FishEye8K and FishEye1Keval) comprises different traffic patterns and conditions, including urban highways, road intersections, various illumination, and viewing angles of the five road object classes in various scales.

Agenda: 

2024 AI CITY CHALLENGE
Challenge Track 4: Road Object Detection in Fish-Eye Cameras

Event Date: 2024/01/22-2024/06
Format: Fill out the AI City Challenge Datasets Request Form online.  One JSON file should be submitted, containing each dictionary, details of a detected object, and corresponding class ID and bounding box.
Venue: on-line
Information: https://www.aicitychallenge.org/