Scalable object detection pipeline for traffic cameras: Application to Tfl JamCams

With CCTV systems being installed in the transport infrastructures of many cities, there is an abundance of data to be extracted from the footage. This paper explores the application of the YOLOv3 object detection algorithm, trained on the COCO dataset, to the Transport for London's (TfL) JamCam feed. The result, open-sourced and publicly available, is a series of easy to deploy Docker pipelines to create, store and serve (through a REST API) data on identified objects on that feed. The pipelines can be deployed to any Linux machine with an NVIDIA GPU to support accelerated computation. We studied how different confidence thresholds affect detections of relevant objects (cars, trucks and pedestrians) in London JamCam scenes. By running the system continuously for three weeks, we built a dataset of more than 2200 detection datapoints for each camera (similar to 6 datapoints an hour). We further visualized the detections on an animated geospatial map, showcasing their effectiveness in identifying traffic patterns typical of an urban city like London, portraying the variation on different object population levels throughout the day.

Automated summary

This study applies the YOLOv3 object detection algorithm to analyze traffic camera feeds in London, creating Docker pipelines for data extraction and visualization. Different confidence thresholds were found to significantly impact object detections, leading to the successful creation of a large-scale dataset.