# Traffic Video Object Detection using Motion Prior

arXiv: https://arxiv.org/abs/2311.10092 · HTML: https://mlsanity.com/paper/2311.10092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 51 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [End-to-End Semi-Supervised Object Detection with Soft Teacher](https://mlsanity.com/paper/2106.09018/) (2021)
- [A Simple Semi-Supervised Learning Framework for Object Detection](https://mlsanity.com/paper/2005.04757/) (2020)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [Deep Feature Flow for Video Recognition](https://mlsanity.com/paper/1611.07715/) (2016)
- [Flow-Guided Feature Aggregation for Video Object Detection](https://mlsanity.com/paper/1703.10025/) (2017)
- [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://mlsanity.com/paper/2004.06002/) (2020)
- [Rethinking Classification and Localization for Object Detection](https://mlsanity.com/paper/1904.06493/) (2019)
- [Region Proposal by Guided Anchoring](https://mlsanity.com/paper/1901.03278/) (2019)
- [Sequence Level Semantics Aggregation for Video Object Detection](https://mlsanity.com/paper/1907.06390/) (2019)
- [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022)
- [Towards High Performance Video Object Detection](https://mlsanity.com/paper/1711.11577/) (2017)
- [Semi-Supervised Video Salient Object Detection Using Pseudo-Labels](https://mlsanity.com/paper/1908.04051/) (2019)
- [Temporal RoI Align for Video Object Recognition](https://mlsanity.com/paper/2109.03495/) (2021)
- [Side-Aware Boundary Localization for More Precise Object Detection](https://mlsanity.com/paper/1912.04260/) (2019)
- [A Three-Stage Self-Training Framework for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2012.00827/) (2020)
- [Background Subtraction in Real Applications: Challenges, Current Models and Future Directions](https://mlsanity.com/paper/1901.03577/) (2019)
- [SCOTCH and SODA: A Transformer Video Shadow Detection Framework](https://mlsanity.com/paper/2211.06885/) (2022)
- [TrafficCAM: A Versatile Dataset for Traffic Flow Segmentation](https://mlsanity.com/paper/2211.09620/) (2022)

## Built on by (most cited first, 4 of 4)

- [MAMBA4D: Efficient Long-Sequence Point Cloud Video Understanding with Disentangled Spatial-Temporal State Space Models](https://mlsanity.com/paper/2405.14338/) (2024)
- [TrafficMOT: A Challenging Dataset for Multi-Object Tracking in Complex Traffic Scenarios](https://mlsanity.com/paper/2311.18839/) (2023)
- [EADReg: Probabilistic Correspondence Generation with Efficient Autoregressive Diffusion Model for Outdoor Point Cloud Registration](https://mlsanity.com/paper/2411.15271/) (2024)
- [Motion-Aware Reinforcement Learning For Object Localization](https://mlsanity.com/paper/2606.21764/) (2026)

## Abstract (arXiv)

Traffic videos inherently differ from generic videos in their stationary camera setup, thus providing a strong motion prior where objects often move in a specific direction over a short time interval. Existing works predominantly employ generic video object detection framework for traffic video object detection, which yield certain advantages such as broad applicability and robustness to diverse scenarios. However, they fail to harness the strength of motion prior to enhance detection accuracy. In this work, we propose two innovative methods to exploit the motion prior and boost the performance of both fully-supervised and semi-supervised traffic video object detection. Firstly, we introduce a new self-attention module that leverages the motion prior to guide temporal information integration in the fully-supervised setting. Secondly, we utilise the motion prior to develop a pseudo-labelling mechanism to eliminate noisy pseudo labels for the semi-supervised setting. Both of our motion-prior-centred methods consistently demonstrates superior performance, outperforming existing state-of-the-art approaches by a margin of 2% in terms of mAP.