Action Detection
233 papers with code • 11 benchmarks • 33 datasets
Action Detection aims to find both where and when an action occurs within a video clip and classify what the action is taking place. Typically results are given in the form of action tublets, which are action bounding boxes linked across time in the video. This is related to temporal localization, which seeks to identify the start and end frame of an action, and action recognition, which seeks only to classify which action is taking place and typically assumes a trimmed video.
Benchmarks
These leaderboards are used to track progress in Action Detection
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Libraries
Use these libraries to find Action Detection models and implementationsDatasets
ActivityNet
Charades
THUMOS14
JHMDB
AVA
MultiTHUMOS
TVSeries
Toyota Smarthome Dataset
Subtasks
Latest papers with no code
A Customer Level Fraudulent Activity Detection Benchmark for Enhancing Machine Learning Model Research and Evaluation
In the field of fraud detection, the availability of comprehensive and privacy-compliant datasets is crucial for advancing machine learning research and developing effective anti-fraud systems.
Leveraging 3D LiDAR Sensors to Enable Enhanced Urban Safety and Public Health: Pedestrian Monitoring and Abnormal Activity Detection
The integration of Light Detection and Ranging (LiDAR) and Internet of Things (IoT) technologies offers transformative opportunities for public health informatics in urban safety and pedestrian well-being.
STMixer: A One-Stage Sparse Action Detector
First, we present a query-based adaptive feature sampling module, which endows the detector with the flexibility of mining a group of discriminative features from the entire spatio-temporal domain.
TE-TAD: Towards Full End-to-End Temporal Action Detection via Time-Aligned Coordinate Expression
In this paper, we investigate that the normalized coordinate expression is a key factor as reliance on hand-crafted components in query-based detectors for temporal action detection (TAD).
Action Detection via an Image Diffusion Process
Action detection aims to localize the starting and ending points of action instances in untrimmed videos, and predict the classes of those instances.
Dual DETRs for Multi-Label Temporal Action Detection
To address this issue, we propose a new Dual-level query-based TAD framework, namely DualDETR, to detect actions from both instance-level and boundary-level.
Benchmarking the Robustness of Temporal Action Detection Models Against Temporal Corruptions
In this paper, we extensively analyze the robustness of seven leading TAD methods and obtain some interesting findings: 1) Existing methods are particularly vulnerable to temporal corruptions, and end-to-end methods are often more susceptible than those with a pre-trained feature extractor; 2) Vulnerability mainly comes from localization error rather than classification error; 3) When corruptions occur in the middle of an action instance, TAD models tend to yield the largest performance drop.
Deep Learning-Assisted Parallel Interference Cancellation for Grant-Free NOMA in Machine-Type Communication
The third framework is designed to accommodate the non-coherent scheme involving a small number of data bits, which simultaneously performs AD and DD.
Detection of Object Throwing Behavior in Surveillance Videos
Second, we compare the performance of different feature extractors for our anomaly detection method on the UCF-Crime and Throwing-Action datasets.
Improving Speaker Assignment in Speaker-Attributed ASR for Real Meeting Applications
Past studies on end-to-end meeting transcription have focused on model architecture and have mostly been evaluated on simulated meeting data.
