Utilizing Video data with machine learning

By: Vajratiya Vajrobol, International Center for AI and Cyber Security Research and Innovations (CCRI), Asia University, Taiwan, vvajratiya@gmail.com

Within the growing field of machine learning, using video data has emerged as a revolutionary pathway for many applications. The process of using the potential of video data includes a sequence of processes, ranging from object detection and activity recognition to complex tasks including temporal analysis. This article provides a process of dealing with video data as follow;

1. Data Collection and Preprocessing

To embark on the machine learning journey with video data, the initial steps involve the collection of a relevant and diverse dataset. Annotate or label the data for supervised learning, ensuring it aligns with your specific task. Preprocess the videos by converting them to a suitable format, extracting frames, and normalizing pixel values. Additional steps such as noise reduction and contrast enhancement contribute to the improvement of data quality[1].

2. Feature Extraction

The essence of effective video analysis lies in extracting pertinent features. Depending on the nature of your task, capture spatial features from individual frames or delve into temporal features across frames. Focusing on deep learning, consider the use of pre-trained models, or deep learning , for feature extraction. The power of transfer learning can be harnessed by leveraging features learned from extensive datasets [2].

3. Model Selection

Choosing an model architecture is vital to the success of your video analysis task. For endeavors involving temporal information, recurrent neural networks (RNNs) or 3D convolutional neural networks (3D CNNs) may be suitable. For frame-based tasks, traditional CNNs may suffice. Explore pre-trained models like C3D, I3D, or two-stream CNNs for efficient transfer learning [3-5].

4. Data Splitting and Training

Divide your video dataset into training and testing sets, maintaining temporal order if necessary. Augment your training data with transformations like rotation and flipping to enhance the model’s generalization capabilities. Train your selected model, adjusting hyperparameters, and closely monitor metrics. Evaluate the model’s performance using relevant metrics such as accuracy, precision, and recall [6].

5. Inference and Deployment

Use the trained model to make predictions on new video data, ensuring that the inference pipeline aligns seamlessly with preprocessing steps. Deploy the model for real-world applications, integrating it into diverse platforms such as applications, servers, or edge devices as per your deployment requirements.

Specific Use Cases

Tailor your approach to the intricacies of ther use case. Object detection in videos benefits from models like You Only Look Once (YOLO) or Faster R-CNN [7]. Action recognition tasks find solace in models such as Two-Stream CNNs or I3D [8-9]. For tasks demanding temporal segmentation, delve into methods like Hidden Markov Models (HMMs) or Long Short-Term Memory networks (LSTMs)[10].

In conclusion, the integration of video data into machine learning workflows represents a dynamic journey, demanding careful for each step. From the foundational stages of data collection and preprocessing to the decisions surrounding model selection and deployment, the process. By adhering to best practices, exploring specialized models, and embracing continuous improvement,

References 👍

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  2. Suresha, M., Kuppa, S., & Raghukumar, D. S. (2020). A study on deep learning spatiotemporal models and feature extraction techniques for video understanding. International Journal of Multimedia Information Retrieval, 9, 81-101.
  3. Dai, W., Chen, Y., Huang, C., Gao, M. K., & Zhang, X. (2019, July). Two-stream convolution neural network with video-stream for action recognition. In 2019 International Joint Conference on Neural Networks (IJCNN) (pp. 1-8). IEEE.
  4. Fan, Y., Lu, X., Li, D., & Liu, Y. (2016, October). Video-based emotion recognition using CNN-RNN and C3D hybrid networks. In Proceedings of the 18th ACM international conference on multimodal interaction (pp. 445-450).
  5. You, J., Shi, P., & Bao, X. (2018, December). Multi-stream I3D network for fine-grained action recognition. In 2018 IEEE 4th Information Technology and Mechatronics Engineering Conference (ITOEC) (pp. 611-614). IEEE.
  6. Karpathy, A., Toderici, G., Shetty, S., Leung, T., Sukthankar, R., & Fei-Fei, L. (2014). Large-scale video classification with convolutional neural networks. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 1725-1732).
  7. Dahirou, Z., & Zheng, M. (2021, July). Motion Detection and Object Detection: Yolo (You Only Look Once). In 2021 7th Annual International Conference on Network and Information Systems for Computers (ICNISC) (pp. 250-257). IEEE.
  8. Liu, G., Zhang, C., Xu, Q., Cheng, R., Song, Y., Yuan, X., & Sun, J. (2020). I3d-shufflenet based human action recognition. Algorithms, 13(11), 301.
  9. Zhao, Y., Man, K. L., Smith, J., Siddique, K., & Guan, S. U. (2020). Improved two-stream model for human action recognition. EURASIP Journal on Image and Video Processing, 2020, 1-9.
  10. Jiang, Z., Crookes, D., Green, B. D., Zhao, Y., Ma, H., Li, L., … & Zhou, H. (2018). Context-aware mouse behavior recognition using hidden markov models. IEEE Transactions on Image Processing, 28(3), 1133-1148.
  11. Poonia, V., Goyal, M. K., Gupta, B. B., Gupta, A. K., Jha, S., & Das, J. (2021). Drought occurrence in different river basins of India and blockchain technology based framework for disaster management. Journal of Cleaner Production312, 127737.
  12. Gupta, B. B., & Sheng, Q. Z. (Eds.). (2019). Machine learning for computer and cyber security: principle, algorithms, and practices. CRC Press.
  13. Singh, A., & Gupta, B. B. (2022). Distributed denial-of-service (DDoS) attacks and defense mechanisms in various web-enabled computing platforms: issues, challenges, and future research directions. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-43.
  14. Almomani, A., Alauthman, M., Shatnawi, M. T., Alweshah, M., Alrosan, A., Alomoush, W., & Gupta, B. B. (2022). Phishing website detection with semantic features based on machine learning classifiers: a comparative study. International Journal on Semantic Web and Information Systems (IJSWIS)18(1), 1-24.

Cite As:

Vajrobol V. (2024) Utilizing Video data with machine learning, Insights2Techinfo, pp.1

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