Hi everyone,
I'm working on a project, "Multimodal Egocentric Action Recognition Based on Context Information," and I'm new to this research area.
My background is in Mechatronics and Control Engineering. Recently, I completed the Deep Learning Specialization courses, which gave me a basic understanding of deep learning concepts. However, I'm finding the concepts around sequence models a bit difficult.
My current goal is to develop a deep-learning algorithm to recognize actions using the Assembly101 dataset (https://assembly-101.github.io). One of my colleagues has already developed an action recognition algorithm using self-supervised learning for sequence data (A Neocortex‑Inspired Locally Recurrent Neural Network). I aim to extend his model to video data, specifically for the Assembly101 dataset. However, I'm unsure where to start and don't feel very confident about it.
I'm looking for guidance and am thrilled to connect with researchers who have experience building deep-learning models for video-based action recognition. I'd love to hear suggestions on adapting my colleague's self-supervised learning model for video data, particularly within the Assembly101 dataset.
Any help or advice would be greatly appreciated.
Thank you!
Zahid Razzaq
To develop a deep learning algorithm for action recognition in videos, such as those in the Assembly101 dataset, you will need to follow several steps. Here’s a high-level guide to help you get started:
- Understand the Dataset: Start by familiarizing yourself with the Assembly101 dataset, which contains multi-view video recordings of people assembling toys. Make sure to understand the annotations and the tasks it supports, such as recognition, anticipation, and temporal segmentation.
- Data Preprocessing: The next step is to prepare the video data for training. This may involve frame extraction, normalization, and data augmentation to increase the diversity of your training set.
- Model Selection: Choose an appropriate deep-learning model. Convolutional Neural Networks (CNNs) are commonly used for spatial feature extraction, while Recurrent Neural Networks (RNNs) or Long Short-Term Memory Networks (LSTMs) can be used for temporal features.
- Feature Extraction: Use the selected model to extract meaningful features from the video frames. For action recognition, you might consider a two-stream approach that processes spatial and temporal information separately.
- Sequence Modeling: Since actions in videos are sequences, you’ll need a model that can handle sequential data. RNNs, LSTMs, or Transformers can be suitable for this task.
- Training: Train your model using the preprocessed data. You’ll need to define a loss function that’s appropriate for action recognition, such as cross-entropy loss for classification tasks.
- Model Evaluation: Validate your model’s performance using metrics like accuracy, precision, recall, and F1 score. It’s also important to perform cross-validation to ensure that your model generalizes well to unseen data.
- Fine-Tuning: Based on the evaluation results, fine-tune your model’s hyperparameters and architecture to improve its performance.
- Comparison or Benchmarking: Compare your model’s performance with existing models or benchmarks on the Assembly101 dataset to gauge its effectiveness.
- Model Deployment: Finally, once you’re satisfied with your model’s performance, you can deploy it for real-world use.
Keep in mind that developing a deep learning algorithm is an iterative process that involves trying different approaches and making improvements. For more comprehensive guidance, consider consulting research papers and resources that delve into deep learning methods for recognizing human actions. You can begin your exploration with the following resources:
- https://paperswithcode.com/dataset/assembly101
- [2403.06810] Deep Learning Approaches for Human Action Recognition in Video Data (arxiv.org)
Chapter Human Activity Recognition in Videos Using Deep Learning
Good luck.!