# Seq-NMS for Video Object Detection

arXiv: https://arxiv.org/abs/1602.08465 · HTML: https://mlsanity.com/paper/1602.08465/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2016.

## Built on by (most cited first, 30 of 68)

- [Crossover Learning for Fast Online Video Instance Segmentation](https://mlsanity.com/paper/2104.05970/) (2021)
- [TransVOD: End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2201.05047/) (2022)
- [Temporal RoI Align for Video Object Recognition](https://mlsanity.com/paper/2109.03495/) (2021)
- [SceneGenie: Scene Graph Guided Diffusion Models for Image Synthesis](https://mlsanity.com/paper/2304.14573/) (2023)
- [End-to-End Video Object Detection with Spatial-Temporal Transformers](https://mlsanity.com/paper/2105.10920/) (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)
- [Compositional Prompt Tuning with Motion Cues for Open-vocabulary Video Relation Detection](https://mlsanity.com/paper/2302.00268/) (2023)
- [YOLOV: Making Still Image Object Detectors Great at Video Object Detection](https://mlsanity.com/paper/2208.09686/) (2022)
- [Video Relation Detection via Tracklet based Visual Transformer](https://mlsanity.com/paper/2108.08669/) (2021)
- [PTSEFormer: Progressive Temporal-Spatial Enhanced TransFormer Towards Video Object Detection](https://mlsanity.com/paper/2209.02242/) (2022)
- [A Guide to Image and Video based Small Object Detection using Deep Learning : Case Study of Maritime Surveillance](https://mlsanity.com/paper/2207.12926/) (2022)
- [DAMO-StreamNet: Optimizing Streaming Perception in Autonomous Driving](https://mlsanity.com/paper/2303.17144/) (2023)
- [MAMBA: Multi-level Aggregation via Memory Bank for Video Object Detection](https://mlsanity.com/paper/2401.09923/) (2024)
- [QueryProp: Object Query Propagation for High-Performance Video Object Detection](https://mlsanity.com/paper/2207.10959/) (2022)
- [LongShortNet: Exploring Temporal and Semantic Features Fusion in Streaming Perception](https://mlsanity.com/paper/2210.15518/) (2022)
- [FAQ: Feature Aggregated Queries for Transformer-based Video Object Detectors](https://mlsanity.com/paper/2303.08319/) (2023)
- [BlockCopy: High-Resolution Video Processing with Block-Sparse Feature Propagation and Online Policies](https://mlsanity.com/paper/2108.09376/) (2021)
- [CML-MOTS: Collaborative Multi-task Learning for Multi-Object Tracking and Segmentation](https://mlsanity.com/paper/2311.00987/) (2023)
- [Video Relation Detection with Trajectory-aware Multi-modal Features](https://mlsanity.com/paper/2101.08165/) (2021)
- [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)
- [Objects do not disappear: Video object detection by single-frame object location anticipation](https://mlsanity.com/paper/2308.04770/) (2023)
- [ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation](https://mlsanity.com/paper/2102.13493/) (2021)
- [Survey: Exploiting Data Redundancy for Optimization of Deep Learning](https://mlsanity.com/paper/2208.13363/) (2022)
- [BoxMask: Revisiting Bounding Box Supervision for Video Object Detection](https://mlsanity.com/paper/2210.06008/) (2022)
- [Real-Time and Accurate Object Detection in Compressed Video by Long Short-term Feature Aggregation](https://mlsanity.com/paper/2103.14529/) (2021)
- [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)
- [Efficient One-stage Video Object Detection by Exploiting Temporal Consistency](https://mlsanity.com/paper/2402.09241/) (2024)

## Abstract (arXiv)

Video object detection is challenging because objects that are easily detected in one frame may be difficult to detect in another frame within the same clip. Recently, there have been major advances for doing object detection in a single image. These methods typically contain three phases: (i) object proposal generation (ii) object classification and (iii) post-processing. We propose a modification of the post-processing phase that uses high-scoring object detections from nearby frames to boost scores of weaker detections within the same clip. We show that our method obtains superior results to state-of-the-art single image object detection techniques. Our method placed 3rd in the video object detection (VID) task of the ImageNet Large Scale Visual Recognition Challenge 2015 (ILSVRC2015).