H.5.9. Scene Analysis
Navid Raisi; Mahdi Rezaei; Behrooz Masoumi
Abstract
Human Activity Recognition (HAR) using computer vision is an expanding field with diverse applications, including healthcare, transportation, and human-computer interaction. While classical approaches such as Support Vector Machines (SVM), Histogram of Oriented Gradients (HOG), and ...
Read More
Human Activity Recognition (HAR) using computer vision is an expanding field with diverse applications, including healthcare, transportation, and human-computer interaction. While classical approaches such as Support Vector Machines (SVM), Histogram of Oriented Gradients (HOG), and Hidden Markov Models (HMM) rely on manually extracted features and struggle with complex motion patterns, deep learning-based models (e.g., Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Transformer-based models) have improved performance but still face challenges in handling occlusions, noisy environments, and computational efficiency. This paper introduces Attention-HAR, a novel deep neural network model designed to enhance HAR performance through three key innovations: Conv3DTranspose for spatial upsampling, ConvLSTM2D for capturing spatiotemporal patterns, and a custom attention mechanism that prioritizes critical frames within sequences. Unlike conventional attention mechanisms, our approach dynamically assigns weights to key frames, reducing the impact of redundant frames and enhancing interpretability and computational efficiency. Experimental results on the UCF-101 dataset demonstrate that Attention-HAR outperforms state-of-the-art models, achieving an accuracy of 97.61%, a precision of 97.95%, a recall of 97.49%, an F1-score of 97.64, and an AUC of 99.9%. With only 1.26 million parameters, the model is computationally efficient and well-suited for deployment on lightweight platforms. These findings suggest that integrating temporal-spatial feature learning with attention mechanisms can significantly improve HAR in dynamic and complex environments.
H.3. Artificial Intelligence
Mohammad Hossein Shayesteh; Behrooz Shahrokhzadeh; Behrooz Masoumi
Abstract
This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, ...
Read More
This paper provides a comprehensive review of the potential of game theory as a solution for sensor-based human activity recognition (HAR) challenges. Game theory is a mathematical framework that models interactions between multiple entities in various fields, including economics, political science, and computer science. In recent years, game theory has been increasingly applied to machine learning challenges, including HAR, as a potential solution to improve recognition performance and efficiency of recognition algorithms. The review covers the shared challenges between HAR and machine learning, compares previous work on traditional approaches to HAR, and discusses the potential advantages of using game theory. It discusses different game theory approaches, including non-cooperative and cooperative games, and provides insights into how they can improve the HAR systems. The authors propose new game theory-based approaches and evaluate their effectiveness compared to traditional approaches. Overall, this review paper contributes to expanding the scope of research in HAR by introducing game-theoretic concepts and solutions to the field and provides valuable insights for researchers interested in applying game-theoretic approaches to HAR.
S. Ahmadluei; K. Faez; B. Masoumi
Abstract
Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there ...
Read More
Deep convolutional neural networks (CNNs) have attained remarkable success in numerous visual recognition tasks. There are two challenges when adopting CNNs in real-world applications: a) Existing CNNs are computationally expensive and memory intensive, impeding their use in edge computing; b) there is no standard methodology for designing the CNN architecture for the intended problem. Network pruning/compression has emerged as a research direction to address the first challenge, and it has proven to moderate CNN computational load successfully. For the second challenge, various evolutionary algorithms have been proposed thus far. The algorithm proposed in this paper can be viewed as a solution to both challenges. Instead of using constant predefined criteria to evaluate the filters of CNN layers, the proposed algorithm establishes evaluation criteria in online manner during network training based on the combination of each filter’s profit in its layer and the next layer. In addition, the novel method suggested that it inserts new filters into the CNN layers. The proposed algorithm is not simply a pruning strategy but determines the optimal number of filters. Training on multiple CNN architectures allows us to demonstrate the efficacy of our approach empirically. Compared to current pruning algorithms, our algorithm yields a network with a remarkable prune ratio and accuracy. Despite the relatively high computational cost of an epoch in the proposed algorithm in pruning, altogether it achieves the resultant network faster than other algorithms.
V. Fazel Asl; B. Karasfi; B. Masoumi
Abstract
In this article, we consider the problems of abnormal behavior detection in a high-crowded environment. One of the main issues in abnormal behavior detection is the complexity of the structure patterns between the frames. In this paper, social force and optical flow patterns are used to prepare the system ...
Read More
In this article, we consider the problems of abnormal behavior detection in a high-crowded environment. One of the main issues in abnormal behavior detection is the complexity of the structure patterns between the frames. In this paper, social force and optical flow patterns are used to prepare the system for training the complexity of the structural patterns. The cycle GAN system has been used to train behavioral patterns. Two models of normal and abnormal behavioral patterns are used to evaluate the accuracy of the system detection. In the case of abnormal patterns used for training, due to the lack of this type of behavioral pattern, which is another challenge in detecting the abnormal behaviors, the geometric techniques are used to augment the patterns. If the normal behavioral patterns are used for training, there is no need to augment the patterns because the normal patterns are sufficient. Then, by using the cycle generative adversarial nets (cycle GAN), the normal and abnormal behaviors training will be considered separately. This system produces the social force and optical flow pattern for normal and abnormal behaviors on the first and second sides. We use the cycle GAN system both to train behavioral patterns and to assess the accuracy of abnormal behaviors detection. In the testing phase, if normal behavioral patterns are used for training, the cycle GAN system should not be able to reconstruct the abnormal behavioral patterns with high accuracy.
