Human Action Recognition from Video Using ML and DL Classifiers

Abstract

Human Action Recognition (HAR) has evolved from traditional handcrafted feature methods to modern data-driven approaches leveraging machine and deep learning. Early systems struggled with generalization in realistic conditions due to occlusions, motion complexity, and background noise. This project overcomes these restrictions by proposing a hybrid framework that merges traditional Machine Learning (ML) with advanced Deep Learning (DL) models to detect human actions from video data. Two fundamental architectures are deployed and contrasted: the Long-term Recurrent Convolutional Network (LRCN), which combines CNNs and LSTMs to capture spatial and temporal patterns, and a streamlined pose-based classifier utilizing Google\'s Move Net for real-time skeleton tracking. Both models are trained and evaluated on benchmark datasets UCF101 and HMDB51. Experimental results demonstrate that while LRCN achieves higher accuracy (~87.6%), the MoveNet model offers superior inference speed and robustness to noise, a making it suitable for real-time applications. The findings highlight key trade-offs between accuracy and latency, providing insights for deploying HAR systems across diverse domains such as surveillance, healthcare, and human-computer interaction.

Introduction

Human Action Recognition (HAR) focuses on identifying and classifying human actions from video data. It's vital for applications such as surveillance, autonomous systems, healthcare, and interactive technologies. Traditional HAR approaches used hand-crafted features (e.g., HOG, optical flow) but struggled with real-world complexity. Modern HAR systems now rely on Machine Learning (ML) and Deep Learning (DL) for more robust performance.

Objective

This research presents a comparative HAR framework using two main architectures:

1. LRCN (Long-term Recurrent Convolutional Network) – Combines CNNs and LSTMs for spatiotemporal modeling of RGB videos.

2. MoveNet-based ML framework – Utilizes 2D pose keypoints for lightweight and privacy-preserving action recognition.

Key Contributions

• Comparative evaluation of LRCN and MoveNet models.

• Real-time prediction using webcam input.

• Modular, scalable pipeline suitable for edge deployment.

• Comprehensive analysis across robustness, latency, and accuracy.

Methodology

Datasets

• UCF101: 13,320 videos, 101 action classes.

• HMDB51: 6,849 videos, 51 actions, high variability.

Preprocessing

• Frame extraction, resizing, normalization.

• Pose keypoint extraction (MoveNet).

• Data augmentation for better generalization.

Model Architectures

1. LRCN (CNN + LSTM)

   • Uses pre-trained CNNs (ResNet, MobileNet) for spatial features.

   • LSTM captures temporal dynamics.

   • High accuracy but higher latency.

2. MoveNet + ML Classifier

   • Extracts 17 pose keypoints per frame.

   • Keypoints fed to Random Forest or shallow neural network.

   • Faster inference, suitable for real-time use.

Training Strategy

• 80-20 split with k-fold validation.

• Metrics: Accuracy, Precision, Recall, F1-Score, Latency.

• Optimizers: Adam/SGD; Loss functions: Cross-Entropy, Focal Loss.

Results & Evaluation

Performance Metrics (UCF101)

• LRCN: Better accuracy and temporal modeling.

• MoveNet: Better speed, suited for real-time webcam input.

Robustness Testing

• MoveNet handles occlusion and noise better due to pose abstraction.

• LRCN is more affected by visual distortions.

Use Cases

• LRCN: Ideal for offline analysis (e.g., forensic, sports analytics).

• MoveNet: Best for real-time, lightweight applications (e.g., gesture control, fitness tracking).

Mathematical & Algorithmic Backbone

• LRCN: CNN extracts spatial features → LSTM models time → Softmax classifies.

• MoveNet: Pose keypoints → Flattened into vectors → Classified via ML (e.g., Random Forest).

• Both use Cross-Entropy loss and regularization for training.

• Evaluation uses Accuracy, Precision, Recall, F1-Score, Top-k Accuracy.

Comparative Insights

Conclusion

This project showcases a resilient and scalable method for Human Action Recognition (HAR) using video data, utilizing a combination of Machine Learning (ML) and Deep Learning (DL) methodologies.The proposed framework addresses the core problem identified in the abstractaccurate recognition of human activities under varied real-world conditionsby employing a hybrid architecture consisting of the Long-term Recurrent Convolutional Network (LRCN) and the MoveNet-based classifier. While LRCN excels at learning spatial-temporal patterns through deep convolutional and recurrent layers, the MoveNet model offers efficient pose-based abstraction through keypoint detection, enabling faster and more resilient recognition under constraints such as occlusion and lighting variation. The system is structured into a modular pipeline that includes video preprocessing, feature extraction, model training, classification, and output visualization. This modularity enables flexibility, rapid testing, and seamless integration between components. The performance of the framework was evaluated using two widely accepted benchmark datasetsUCF101 and HMDB51Results demonstrated that the LRCN model attained an 87.6% accuracy rate, excelling in precision and recall for intricate motion patterns. Conversely, the MoveNet classifier demonstrated swifter inference speeds, achieving an accuracy of 82.4% with a latency of under 50 milliseconds per frame. These results confirm that the framework offers a suitable balance between accuracy and real-time performance, depending on the application requirements. By integrating preprocessing steps such as frame normalization and pose keypoint extraction, the framework ensures that input data is clean and structured, contributing to model stability and performance. The user interface further enhances accessibility, allowing even non-technical users to upload video data, select models, and view prediction outputs. Alongside thorough unit and integration testing, this ensures the resilience and user-friendliness of the system across various applications including surveillance, sports analysis, and healthcare monitoring. For future improvements, the system could be advanced by incorporating multi-person tracking, gesture recognition at a granular level, and forecasting temporal actions utilizing sophisticated deep learning architectures like transformersAdditional improvements can include deployment on mobile and embedded platforms for edge-based inference and the fusion of audio-visual data for multi-modal action recognition. These enhancements will broaden the system’s applicability in real-time, dynamic environments. In conclusion, the proposed HAR system effectively addresses the limitations of traditional action recognition methods by combining spatial, temporal, and skeletal data representations into a unified and adaptable architecture. It delivers accurate, efficient, and real-time recognition capabilities that align with the goals outlined in the problem statement, offering a strong foundation for future developments in intelligent human-computer interaction systems

References

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Copyright

Copyright © 2025 Mrs. Subhashree D C, Ms. Sanjana Shettar. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.