# Object Detection from Video Tubelets with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1604.04053 · HTML: https://mlsanity.com/paper/1604.04053/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Apr 2016.

## Built on by (most cited first, 30 of 40)

- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [A Comprehensive Survey of Scene Graphs: Generation and Application](https://mlsanity.com/paper/2104.01111/) (2021)
- [Scene Graph Generation: A Comprehensive Survey](https://mlsanity.com/paper/2201.00443/) (2022)
- [3D Object Detection from Images for Autonomous Driving: A Survey](https://mlsanity.com/paper/2202.02980/) (2022)
- [Semi-Decentralized Federated Learning with Cooperative D2D Local Model Aggregations](https://mlsanity.com/paper/2103.10481/) (2021)
- [TF-Blender: Temporal Feature Blender for Video Object Detection](https://mlsanity.com/paper/2108.05821/) (2021)
- [Deep Learning for UAV-based Object Detection and Tracking: A Survey](https://mlsanity.com/paper/2110.12638/) (2021)
- [DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving](https://mlsanity.com/paper/2303.17144/) (2023)
- [QueryProp: Object Query Propagation for High-Performance Video Object Detection](https://mlsanity.com/paper/2207.10959/) (2022)
- [FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors](https://mlsanity.com/paper/2303.08319/) (2023)
- [Boggart: Towards General-Purpose Acceleration of Retrospective Video Analytics](https://mlsanity.com/paper/2106.15315/) (2021)
- [Towards Balanced Learning for Instance Recognition](https://mlsanity.com/paper/2108.10175/) (2021)
- [CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2311.00987/) (2023)
- [Spatio-Temporal Learnable Proposals for End-to-End Video Object Detection](https://mlsanity.com/paper/2210.02368/) (2022)
- [Automated Rip Current Detection with Region based Convolutional Neural Networks](https://mlsanity.com/paper/2102.02902/) (2021)
- [Few-Shot Video Object Detection](https://mlsanity.com/paper/2104.14805/) (2021)
- [Objects do not disappear: Video object detection by single-frame object location anticipation](https://mlsanity.com/paper/2308.04770/) (2023)
- [Multi-layered Semantic Representation Network for Multi-label Image Classification](https://mlsanity.com/paper/2106.11596/) (2021)
- [BoxMask: Revisiting Bounding Box Supervision for Video Object Detection](https://mlsanity.com/paper/2210.06008/) (2022)
- [DFA: Dynamic Feature Aggregation for Efficient Video Object Detection](https://mlsanity.com/paper/2210.00588/) (2022)
- [Flying Bird Object Detection Algorithm in Surveillance Video Based on Motion Information](https://mlsanity.com/paper/2301.01917/) (2023)
- [DarkVision: A Benchmark for Low-light Image/Video Perception](https://mlsanity.com/paper/2301.06269/) (2023)
- [Few-shot Online Anomaly Detection and Segmentation](https://mlsanity.com/paper/2403.18201/) (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)
- [SSVOD: Semi-Supervised Video Object Detection with Sparse Annotations](https://mlsanity.com/paper/2309.01391/) (2023)
- [MVP: Motion Vector Propagation for Zero-Shot Video Object Detection](https://mlsanity.com/paper/2509.18388/) (2025)
- [An Introduction to Robust Graph Convolutional Networks](https://mlsanity.com/paper/2103.14807/) (2021)
- [Video Relationship Detection Using Mixture of Experts](https://mlsanity.com/paper/2403.03994/) (2024)
- [SSGA-Net: Stepwise Spatial Global-local Aggregation Networks for for Autonomous Driving](https://mlsanity.com/paper/2405.18857/) (2024)

## Abstract (arXiv)

Deep Convolution Neural Networks (CNNs) have shown impressive performance in various vision tasks such as image classification, object detection and semantic segmentation. For object detection, particularly in still images, the performance has been significantly increased last year thanks to powerful deep networks (e.g. GoogleNet) and detection frameworks (e.g. Regions with CNN features (R-CNN)). The lately introduced ImageNet task on object detection from video (VID) brings the object detection task into the video domain, in which objects' locations at each frame are required to be annotated with bounding boxes. In this work, we introduce a complete framework for the VID task based on still-image object detection and general object tracking. Their relations and contributions in the VID task are thoroughly studied and evaluated. In addition, a temporal convolution network is proposed to incorporate temporal information to regularize the detection results and shows its effectiveness for the task.