Video Object Detection Edit Task
Computer Vision • Object Detection
About
Video object detection is the task of detecting objects from a video as opposed to images.
( Image credit: Learning Motion Priors for Efficient Video Object Detection )
Benchmarks
| TREND | DATASET | BEST METHOD | PAPER TITLE | PAPER | CODE | COMPARE |
|---|---|---|---|---|---|---|
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Datasets
Greatest papers with code
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
CVPR 2020
• tensorflow/models
• 
In this paper we propose a method that leverages temporal context from the unlabeled frames of a novel camera to improve performance at that camera.
Looking Fast and Slow: Memory-Guided Mobile Video Object Detection
Models and examples built with TensorFlow
Ranked #11 on
Video Object Detection
on ImageNet VID
OBJECT RECOGNITION REAL-TIME OBJECT DETECTION VIDEO OBJECT DETECTION
Mobile Video Object Detection with Temporally-Aware Feature Maps
CVPR 2018
• tensorflow/models
•
This paper introduces an online model for object detection in videos designed to run in real-time on low-powered mobile and embedded devices.
TSM: Temporal Shift Module for Efficient Video Understanding
ICCV 2019
• MIT-HAN-LAB/temporal-shift-module
• 
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
Ranked #4 on
Action Recognition
on Something-Something V2
(using extra training data)
ACTION CLASSIFICATION ACTION RECOGNITION VIDEO OBJECT DETECTION VIDEO RECOGNITION VIDEO UNDERSTANDING
Sequence Level Semantics Aggregation for Video Object Detection
ICCV 2019
• open-mmlab/mmtracking
•
In this work, we argue that aggregating features in the full-sequence level will lead to more discriminative and robust features for video object detection.
Ranked #3 on
Video Object Detection
on ImageNet VID
Flow-Guided Feature Aggregation for Video Object Detection
ICCV 2017
• open-mmlab/mmtracking
•
The accuracy of detection suffers from degenerated object appearances in videos, e. g., motion blur, video defocus, rare poses, etc.
Ranked #7 on
Video Object Detection
on ImageNet VID
Optimizing Video Object Detection via a Scale-Time Lattice
CVPR 2018
• guanfuchen/video_obj
•
High-performance object detection relies on expensive convolutional networks to compute features, often leading to significant challenges in applications, e. g. those that require detecting objects from video streams in real time.
Memory Enhanced Global-Local Aggregation for Video Object Detection
CVPR 2020
• Scalsol/mega.pytorch
•
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
Ranked #2 on
Video Object Detection
on ImageNet VID
Relation Distillation Networks for Video Object Detection
ICCV 2019
• Scalsol/mega.pytorch
•
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
Vision Meets Drones: Past, Present and Future
We provide a large-scale drone captured dataset, VisDrone, which includes four tracks, i. e., (1) image object detection, (2) video object detection, (3) single object tracking, and (4) multi-object tracking.