VIDEO-BASED ACTION RECOGNITION USING SPATIOTEMPORAL DEEP LEARNING MODELS

International Journal of Computer Science (IJCS) Published by SK Research Group of Companies (SKRGC)

Abstract

Video-based action recognition is a crucial task in computer vision with applications spanning surveillance, sports analytics, human-computer interaction, and autonomous systems. This paper explores the application of spatiotemporal deep learning models for action recognition, leveraging advancements in neural network architectures to analyze video data effectively. Traditional approaches often process spatial and temporal dimensions independently, limiting their ability to capture complex motion patterns and contextual relationships. In contrast, spatiotemporal deep learning models integrate spatial and temporal features simultaneously, enabling robust recognition of dynamic actions. The study highlights key methods, including convolutional neural networks (CNNs) for spatial feature extraction and recurrent neural networks (RNNs) or 3D convolutional networks (3D-CNNs) for temporal modelling. It also delves into transformer-based architectures and attention mechanisms, which enhance model performance by selectively focusing on salient regions and time steps. A comprehensive evaluation is conducted using benchmark datasets such as UCF101, HMDB51, and Kinetics, comparing the efficacy of various architectures in terms of accuracy, computational efficiency, and scalability. Additionally, the impact of pretraining on large-scale datasets, multimodal fusion (e.g., combining visual and audio data), and data augmentation techniques are discussed to improve generalization. Challenges such as handling occlusions, recognizing subtle motions, and reducing computational overhead are addressed. The results demonstrate the potential of spatiotemporal deep learning models to outperform traditional methods, paving the way for more accurate and efficient video-based action recognition systems. Future directions include exploring unsupervised and semi-supervised learning approaches, real-time inference capabilities, and domain-specific adaptations for broader applications.

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Keywords

Spatiotemporal Action Recognition, Deep Learning for Video Analysis, Human Activity Recognition (HAR), Temporal Convolutional Networks (TCN), 3D Convolutional Neural Networks (3D-CNNs).