ARRNET: Action recognition through recurrent neural networks

2016 ◽  
Author(s):  
Kumaresh Krishnan ◽  
Nikita Prabhu ◽  
R. Venkatesh Babu
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Recurrent Neural Networks

scholarly journals Action recognition on video using recurrent neural networks

2017 ◽  
Vol 8 (4) ◽  
pp. 327-345
Author(s):  
Aleksandr Buyko ◽  
◽  
Andrey Vinogradov ◽  
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Recurrent Neural Networks

Skeleton-Based Action Recognition with Joint Coordinates as Feature Using Neural Oblivious Decision Ensembles

2021 ◽  
Author(s):  
Fakhrul Aniq Hakimi Nasrul ’Alam ◽  
Mohd. Ibrahim Shapiai ◽  
Uzma Batool ◽  
Ahmad Kamal Ramli ◽  
Khairil Ashraf Elias
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Recurrent Neural Networks ◽  
State Of The Art ◽  
Structured Data ◽  
Joint Coordinates ◽  
Label Prediction ◽  
Within Subjects ◽  
High Degree ◽  
Behaviour Recognition

Recognition of human behavior is critical in video monitoring, human-computer interaction, video comprehension, and virtual reality. The key problem with behaviour recognition in video surveillance is the high degree of variation between and within subjects. Numerous studies have suggested background-insensitive skeleton-based as the proven detection technique. The present state-of-the-art approaches to skeleton-based action recognition rely primarily on Recurrent Neural Networks (RNN) and Convolution Neural Networks (CNN). Both methods take dynamic human skeleton as the input to the network. We chose to handle skeleton data differently, relying solely on its skeleton joint coordinates as the input. The skeleton joints’ positions are defined in (x, y) coordinates. In this paper, we investigated the incorporation of the Neural Oblivious Decision Ensemble (NODE) into our proposed action classifier network. The skeleton is extracted using a pose estimation technique based on the Residual Network (ResNet). It extracts the 2D skeleton of 18 joints for each detected body. The joint coordinates of the skeleton are stored in a table in the form of rows and columns. Each row represents the position of the joints. The structured data are fed into NODE for label prediction. With the proposed network, we obtain 97.5% accuracy on RealWorld (HAR) dataset. Experimental results show that the proposed network outperforms one the state-of-the-art approaches by 1.3%. In conclusion, NODE is a promising deep learning technique for structured data analysis as compared to its machine learning counterparts such as the GBDT packages; Catboost, and XGBoost.

scholarly journals Compressing Recurrent Neural Networks with Tensor Ring for Action Recognition

2019 ◽  
Vol 33 ◽  
pp. 4683-4690 ◽  
Author(s):  
Yu Pan ◽  
Jing Xu ◽  
Maolin Wang ◽  
Jinmian Ye ◽  
Fei Wang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Recurrent Neural Networks ◽  
Input Data ◽  
Short Term Memory ◽  
Computational Cost ◽  
Tensor Decomposition ◽  
Decomposition Methods ◽  
Low Rank ◽  
Sequential Data

Recurrent Neural Networks (RNNs) and their variants, such as Long-Short Term Memory (LSTM) networks, and Gated Recurrent Unit (GRU) networks, have achieved promising performance in sequential data modeling. The hidden layers in RNNs can be regarded as the memory units, which are helpful in storing information in sequential contexts. However, when dealing with high dimensional input data, such as video and text, the input-to-hidden linear transformation in RNNs brings high memory usage and huge computational cost. This makes the training of RNNs very difficult. To address this challenge, we propose a novel compact LSTM model, named as TR-LSTM, by utilizing the low-rank tensor ring decomposition (TRD) to reformulate the input-to-hidden transformation. Compared with other tensor decomposition methods, TR-LSTM is more stable. In addition, TR-LSTM can complete an end-to-end training and also provide a fundamental building block for RNNs in handling large input data. Experiments on real-world action recognition datasets have demonstrated the promising performance of the proposed TR-LSTM compared with the tensor-train LSTM and other state-of-the-art competitors.

scholarly journals Action recognition on video using recurrent neural networks

2017 ◽  
Vol 8 (4) ◽  
pp. 327-344
Author(s):  
Aleksandr Buyko ◽  
◽  
Andrey Vinogradov ◽  
Keyword(s):  
Neural Networks ◽  
Action Recognition ◽  
Recurrent Neural Networks
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