# Global Temporal Representation based CNNs for Infrared Action Recognition

arXiv: https://arxiv.org/abs/1909.08287 · HTML: https://mlsanity.com/paper/1909.08287/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2019.

## Built on by (most cited first, 16 of 16)

- [Enriching Local and Global Contexts for Temporal Action Localization](https://mlsanity.com/paper/2107.12960/) (2021)
- [Cross-Modal Causal Relational Reasoning for Event-Level Visual Question Answering](https://mlsanity.com/paper/2207.12647/) (2022)
- [SkeletonMAE: Graph-based Masked Autoencoder for Skeleton Sequence Pre-training](https://mlsanity.com/paper/2307.08476/) (2023)
- [TCGL: Temporal Contrastive Graph for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2112.03587/) (2021)
- [Causal Reasoning Meets Visual Representation Learning: A Prospective Study](https://mlsanity.com/paper/2204.12037/) (2022)
- [Visual Causal Scene Refinement for Video Question Answering](https://mlsanity.com/paper/2305.04224/) (2023)
- [Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey](https://mlsanity.com/paper/2211.10412/) (2022)
- [Urban Regional Function Guided Traffic Flow Prediction](https://mlsanity.com/paper/2303.09789/) (2023)
- [DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback](https://mlsanity.com/paper/2306.07553/) (2023)
- [Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval](https://mlsanity.com/paper/2101.00820/) (2021)
- [CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning](https://mlsanity.com/paper/2306.17462/) (2023)
- [MEIA: Multimodal Embodied Perception and Interaction in Unknown Environments](https://mlsanity.com/paper/2402.00290/) (2024)
- [Self-supervised Contrastive Learning for Audio-Visual Action Recognition](https://mlsanity.com/paper/2204.13386/) (2022)
- [VCD: Visual Causality Discovery for Cross-Modal Question Reasoning](https://mlsanity.com/paper/2304.08083/) (2023)
- [Day2Dark: Pseudo-Supervised Activity Recognition beyond Silent Daylight](https://mlsanity.com/paper/2212.02053/) (2022)
- [ActAR: Actor-Driven Pose Embeddings for Video Action Recognition](https://mlsanity.com/paper/2204.08671/) (2022)

## Abstract (arXiv)

Infrared human action recognition has many advantages, i.e., it is insensitive to illumination change, appearance variability, and shadows. Existing methods for infrared action recognition are either based on spatial or local temporal information, however, the global temporal information, which can better describe the movements of body parts across the whole video, is not considered. In this letter, we propose a novel global temporal representation named optical-flow stacked difference image (OFSDI) and extract robust and discriminative feature from the infrared action data by considering the local, global, and spatial temporal information together. Due to the small size of the infrared action dataset, we first apply convolutional neural networks on local, spatial, and global temporal stream respectively to obtain efficient convolutional feature maps from the raw data rather than train a classifier directly. Then these convolutional feature maps are aggregated into effective descriptors named three-stream trajectory-pooled deep-convolutional descriptors by trajectory-constrained pooling. Furthermore, we improve the robustness of these features by using the locality-constrained linear coding (LLC) method. With these features, a linear support vector machine (SVM) is adopted to classify the action data in our scheme. We conduct the experiments on infrared action recognition datasets InfAR and NTU RGB+D. The experimental results show that the proposed approach outperforms the representative state-of-the-art handcrafted features and deep learning features based methods for the infrared action recognition.