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Activity Recognition

28 papers with code · Computer Vision

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Temporal Relational Reasoning in Videos

ECCV 2018 metalbubble/TRN-pytorch

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales.

ACTIVITY RECOGNITION COMMON SENSE REASONING HUMAN-OBJECT INTERACTION DETECTION RELATIONAL REASONING

TS-LSTM and Temporal-Inception: Exploiting Spatiotemporal Dynamics for Activity Recognition

30 Mar 2017chihyaoma/Activity-Recognition-with-CNN-and-RNN

Building upon our experimental results, we then propose and investigate two different networks to further integrate spatiotemporal information: 1) temporal segment RNN and 2) Inception-style Temporal-ConvNet. We demonstrate that using both RNNs (using LSTMs) and Temporal-ConvNets on spatiotemporal feature matrices are able to exploit spatiotemporal dynamics to improve the overall performance.

ACTION CLASSIFICATION ACTIVITY RECOGNITION VIDEO CLASSIFICATION VIDEO UNDERSTANDING

Multivariate LSTM-FCNs for Time Series Classification

14 Jan 2018titu1994/LSTM-FCN

Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance.

ACTION RECOGNITION ACTIVITY RECOGNITION TIME SERIES TIME SERIES CLASSIFICATION

Hierarchical Attentive Recurrent Tracking

NeurIPS 2017 akosiorek/hart

Class-agnostic object tracking is particularly difficult in cluttered environments as target specific discriminative models cannot be learned a priori. Inspired by how the human visual cortex employs spatial attention and separate "where" and "what" processing pathways to actively suppress irrelevant visual features, this work develops a hierarchical attentive recurrent model for single object tracking in videos.

ACTIVITY RECOGNITION OBJECT TRACKING

Object Level Visual Reasoning in Videos

ECCV 2018 fabienbaradel/object_level_visual_reasoning

Human activity recognition is typically addressed by detecting key concepts like global and local motion, features related to object classes present in the scene, as well as features related to the global context. The next open challenges in activity recognition require a level of understanding that pushes beyond this and call for models with capabilities for fine distinction and detailed comprehension of interactions between actors and objects in a scene.

HUMAN ACTIVITY RECOGNITION OBJECT DETECTION VISUAL REASONING

Interpretable 3D Human Action Analysis with Temporal Convolutional Networks

14 Apr 2017TaeSoo-Kim/TCNActionRecognition

In this work, we propose to use a new class of models known as Temporal Convolutional Neural Networks (TCN) for 3D human action recognition. We provide our strategy in re-designing the TCN with interpretability in mind and how such characteristics of the model is leveraged to construct a powerful 3D activity recognition method.

3D HUMAN ACTION RECOGNITION ACTIVITY RECOGNITION

Hierarchical Deep Temporal Models for Group Activity Recognition

9 Jul 2016mostafa-saad/deep-activity-rec

We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. In order to model both person-level and group-level dynamics, we present a 2-stage deep temporal model for the group activity recognition problem.

GROUP ACTIVITY RECOGNITION

M-PACT: An Open Source Platform for Repeatable Activity Classification Research

16 Apr 2018MichiganCOG/M-PACT

These hurdles include switching between multiple deep learning libraries and the development of boilerplate experimental pipelines. We present M-PACT to overcome existing issues by removing the need to develop boilerplate code which allows users to quickly prototype action classification models while leveraging existing state-of-the-art (SOTA) models available in the platform.

ACTION CLASSIFICATION ACTIVITY RECOGNITION

Fine-grained Activity Recognition in Baseball Videos

9 Apr 2018piergiaj/mlb-youtube

In this paper, we introduce a challenging new dataset, MLB-YouTube, designed for fine-grained activity detection. The dataset contains two settings: segmented video classification as well as activity detection in continuous videos.

ACTION DETECTION ACTIVITY RECOGNITION VIDEO CLASSIFICATION

Analysis of Hand Segmentation in the Wild

CVPR 2018 aurooj/Hand-Segmentation-in-the-Wild

In the quest for robust hand segmentation methods, we evaluated the performance of the state of the art semantic segmentation methods, off the shelf and fine-tuned, on existing datasets. Finally, we annotate a subset of the EgoHands dataset for fine-grained action recognition and show that an accuracy of 58.6% can be achieved by just looking at a single hand pose which is much better than the chance level (12.5%).

ACTION RECOGNITION ACTIVITY RECOGNITION HAND SEGMENTATION SEMANTIC SEGMENTATION