# RoadTrack: Realtime Tracking of Road Agents in Dense and Heterogeneous Environments

## TL;DR

RoadTrack is a real-time, collision-aware tracking algorithm for diverse road agents in dense traffic, outperforming previous methods in accuracy and speed, suitable for complex urban scenarios.

## Contribution

The paper introduces a novel motion model, SimCAI, for predicting heterogeneous road-agent movements, enabling accurate and fast real-time tracking in dense traffic environments.

## Key findings

- Achieves 75.8% accuracy on dense traffic dataset.
- Operates at approximately 30 fps, four times faster than prior methods.
- Outperforms state-of-the-art algorithms by at least 5.2% in accuracy.

## Abstract

We present a realtime tracking algorithm, RoadTrack, to track heterogeneous road-agents in dense traffic videos. Our approach is designed for traffic scenarios that consist of different road-agents such as pedestrians, two-wheelers, cars, buses, etc. sharing the road. We use the tracking-by-detection approach where we track a road-agent by matching the appearance or bounding box region in the current frame with the predicted bounding box region propagated from the previous frame. RoadTrack uses a novel motion model called the Simultaneous Collision Avoidance and Interaction (SimCAI) model to predict the motion of road-agents by modeling collision avoidance and interactions between the road-agents for the next frame. We demonstrate the advantage of RoadTrack on a dataset of dense traffic videos and observe an accuracy of 75.8% on this dataset, outperforming prior state-of-the-art tracking algorithms by at least 5.2%. RoadTrack operates in realtime at approximately 30 fps and is at least 4 times faster than prior tracking algorithms on standard tracking datasets.