Video Object Detection
66 papers with code • 7 benchmarks • 10 datasets
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
These leaderboards are used to track progress in Video Object Detection
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Libraries
Use these libraries to find Video Object Detection models and implementationsDatasets
Waymo Open Dataset
EPIC-KITCHENS-55
ImageNet VID
YT-BB
OAK
THGP
VISEM-Tracking
Most implemented papers
Sequence Level Semantics Aggregation for Video Object Detection
open-mmlab/mmtracking
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ICCV 2019
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.
Relation Distillation Networks for Video Object Detection
Scalsol/mega.pytorch
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ICCV 2019
In this paper, we introduce a new design to capture the interactions across the objects in spatio-temporal context.
Detection and Tracking Meet Drones Challenge
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.
Plug & Play Convolutional Regression Tracker for Video Object Detection
As the tracker reuses the features from the detector, it is a very light-weighted increment to the detection network.
Memory Enhanced Global-Local Aggregation for Video Object Detection
Scalsol/mega.pytorch
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CVPR 2020
We argue that there are two important cues for humans to recognize objects in videos: the global semantic information and the local localization information.
Instance-aware, Context-focused, and Memory-efficient Weakly Supervised Object Detection
NVlabs/wetectron
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CVPR 2020
Weakly supervised learning has emerged as a compelling tool for object detection by reducing the need for strong supervision during training.
Spatio-temporal Prompting Network for Robust Video Feature Extraction
guanxiongsun/vfe.pytorch
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ICCV 2023
Then, these video prompts are prepended to the patch embeddings of the current frame as the updated input for video feature extraction.
Seq-NMS for Video Object Detection
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.
Structure-measure: A New Way to Evaluate Foreground Maps
Our new measure simultaneously evaluates region-aware and object-aware structural similarity between a SM and a GT map.
Optimizing Video Object Detection via a Scale-Time Lattice
guanfuchen/video_obj
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CVPR 2018
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.
