# Object Detection in Videos with Tubelet Proposal Networks

arXiv: https://arxiv.org/abs/1702.06355 · HTML: https://mlsanity.com/paper/1702.06355/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Feb 2017.

## Built on by (most cited first, 24 of 24)

- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Learning to Track with Object Permanence](https://mlsanity.com/paper/2103.14258/) (2021)
- [MPPNet: Multi-Frame Feature Intertwining with Proxy Points for 3D Temporal Object Detection](https://mlsanity.com/paper/2205.05979/) (2022)
- [Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety](https://mlsanity.com/paper/2104.14235/) (2021)
- [Object Permanence Emerges in a Random Walk along Memory](https://mlsanity.com/paper/2204.01784/) (2022)
- [INT: Towards Infinite-frames 3D Detection with An Efficient Framework](https://mlsanity.com/paper/2209.15215/) (2022)
- [MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection](https://mlsanity.com/paper/2401.09923/) (2024)
- [Tubelet-Contrastive Self-Supervision for Video-Efficient Generalization](https://mlsanity.com/paper/2303.11003/) (2023)
- [MaCLR: Motion-aware Contrastive Learning of Representations for Videos](https://mlsanity.com/paper/2106.09703/) (2021)
- [Have We Ever Encountered This Before? Retrieving Out-of-Distribution Road Obstacles from Driving Scenes](https://mlsanity.com/paper/2309.04302/) (2023)
- [Spatio-Temporal Learnable Proposals for End-to-End Video Object Detection](https://mlsanity.com/paper/2210.02368/) (2022)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [CFTrack: Center-based Radar and Camera Fusion for 3D Multi-Object Tracking](https://mlsanity.com/paper/2107.05150/) (2021)
- [Survey: Exploiting Data Redundancy for Optimization of Deep Learning](https://mlsanity.com/paper/2208.13363/) (2022)
- [Efficient One-stage Video Object Detection by Exploiting Temporal Consistency](https://mlsanity.com/paper/2402.09241/) (2024)
- [Identity-Consistent Aggregation for Video Object Detection](https://mlsanity.com/paper/2308.07737/) (2023)
- [Learning to Track Object Position through Occlusion](https://mlsanity.com/paper/2106.10766/) (2021)
- [3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation](https://mlsanity.com/paper/2110.02531/) (2021)
- [Context Enhanced Transformer for Single Image Object Detection](https://mlsanity.com/paper/2312.14492/) (2023)
- [Spatiotemporal Object Detection for Improved Aerial Vehicle Detection in Traffic Monitoring](https://mlsanity.com/paper/2410.13616/) (2024)
- [Object Permanence in Object Detection Leveraging Temporal Priors at Inference Time](https://mlsanity.com/paper/2211.15505/) (2022)
- [Spatiotemporal Learning with Context-aware Video Tubelets for Ultrasound Video Analysis](https://mlsanity.com/paper/2503.17475/) (2025)
- [Instant Video Models: Universal Adapters for Stabilizing Image-Based Networks](https://mlsanity.com/paper/2512.03014/) (2025)
- [Albireo: Adaptive, Energy-Efficient Inference Framework for Video Object Detection on the Edge](https://mlsanity.com/paper/2609.29648/) (2026)

## Abstract (arXiv)

Object detection in videos has drawn increasing attention recently with the introduction of the large-scale ImageNet VID dataset. Different from object detection in static images, temporal information in videos is vital for object detection. To fully utilize temporal information, state-of-the-art methods are based on spatiotemporal tubelets, which are essentially sequences of associated bounding boxes across time. However, the existing methods have major limitations in generating tubelets in terms of quality and efficiency. Motion-based methods are able to obtain dense tubelets efficiently, but the lengths are generally only several frames, which is not optimal for incorporating long-term temporal information. Appearance-based methods, usually involving generic object tracking, could generate long tubelets, but are usually computationally expensive. In this work, we propose a framework for object detection in videos, which consists of a novel tubelet proposal network to efficiently generate spatiotemporal proposals, and a Long Short-term Memory (LSTM) network that incorporates temporal information from tubelet proposals for achieving high object detection accuracy in videos. Experiments on the large-scale ImageNet VID dataset demonstrate the effectiveness of the proposed framework for object detection in videos.