In computer vision technology, human motion recognition is an active area of research with enormous applications. In recent years, automatic human motion recognition has become one of the most challenging problems in computer vision.
In visual surveillance systems, human action recognition helps to recognize dangerous human activities, and in autonomous navigation systems, human action recognition assists in discerning human behaviors for safe operation. Human motion recognition is significant in several applications in artificial intelligence such as video surveillance, computer games, robotics, and human interactions.
Owing to the tremendous success of deep learning in various computer vision tasks, deep learning technology is also applied in human action recognition with its essential advantages, such as learning representations with the high-level abstraction of complex data, distributed representation, and utilizing unlabelled data.
Some deep learning models with high potential for human motion recognition are RBM-based Models, Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks, and Generative Adversarial Networks. Deep learning-based human motion recognition utilizes various data modalities, such as RGB, skeleton, depth, infrared, point cloud, event stream, audio, acceleration, radar, and WiFi signal.
RBM-based Models - RBM-based deep learning models are one of the earliest successful deep learning models utilized for human motion recognition. These models are employed as stacking multiple RBMs for human action recognition. The main importance of exploiting RBM-based models for human motion recognition is to decrease sensor data-s dimensionality and extract beneficial features in an unsupervised way. Attractive deep learning models based on RBMs are Deep Belief Networks (DBNs), Deep Boltzmann Machines (DBMs), and Convolutional Boltzmann Machines (CBMs).
Auto-encoders - Auto-encoders and their variants have been broadly utilized for human motion recognition due to their outstanding performance and representation ability. Some of the auto-encoders variants are Sparse Autoencoder (SAE), Denoising Autoencoder (DAE), and Variational Autoencoder (VAE).
Convolutional Neural Networks - Owing to the excellent performance of Convolutional Neural Networks in diverse domains especially image classification. Several variants of CNNs have been developed for human motion recognition.
Recurrent Neural Networks - RNNs have been widely utilized for human motion recognition by resolving the sequential problem. Long-Short Term Memory (LSTM), Gated Recurrent Units (GRUs), and deep RNN (DRNN) are RNN variants applied for human motion recognition.
Generative Adversarial Networks - In human motion recognition, Generative Adversarial Networks are applied to solve the challenge by collecting labeled data. Some of the variants of GAN are DCGAN, CycleGAN, InfoGAN, ProGAN, WGAN, SAGAN, EBGAN, BEGAN, and Style-GAN.
High Accuracy: Deep learning models have demonstrated high accuracy in recognizing and classifying various human motions. They can handle complex patterns and subtle movement variations, making them suitable for precise motion analysis.
Robustness: Deep learning models can recognize human motions in diverse environments under different lighting conditions and with varying camera angles. They are robust to noise and can adapt to real-world scenarios.
Temporal Understanding: Recurrent neural networks, including LSTM and GRU variants, can capture temporal dependencies in motion sequences. It enables the recognition of time-dependent activities and complex motion patterns.
Adaptability: These models can adapt to different users and environments. They can be fine-tuned or retrained to recognize specific users unique motion patterns or adapt to environmental changes.
Accessibility and Inclusivity: Motion recognition can provide accessible interfaces for disabled individuals. It enables gesture-based control and communication, making technology more inclusive.
Reduced Human Intervention: In applications such as surveillance and security, human motion recognition reduces the need for continuous human monitoring. It can trigger alarms or alerts when suspicious motions are detected.
Ongoing Advancements: Deep learning continuously evolves, leading to improved models and techniques for human motion recognition. This ongoing research ensures that the technology remains at the forefront of innovation.
Data Quality and Quantity: Collecting and annotating large, diverse, high-quality motion datasets can be time-consuming and expensive.
Privacy Concerns: Address privacy issues associated with capturing and analyzing individuals movements, especially in surveillance and healthcare applications.
Generalization: Achieve robust generalization across different users, environments, and motion variations, which can be challenging.
Complex Activities: Recognize complex and nuanced human motions, such as fine-grained hand gestures or subtle facial expressions.
Continuous Learning: Maintain accurate recognition over time in dynamic environments or as user behaviors change.
Bias and Fairness: Mitigate bias in training data and ensure fairness across demographic groups to avoid discriminatory outcomes.
Human Variability: Account for the natural variability in human movements, making recognition more challenging.
Occlusions and Interference: Obstacles or partial occlusions in the environment can hinder accurate motion recognition. These issues may require additional techniques like object tracking or multi-modal sensor fusion.
Multi-modal Fusion for Improved Accuracy: Research explores data fusion from multiple sensors, such as RGB cameras, depth sensors, and accelerometers, to enhance motion recognition accuracy and robustness.
Few-shot and Zero-shot Learning: Investigating techniques that enable deep learning models to recognize new, unseen human motions with minimal training data, potentially using knowledge transfer from related tasks.
Edge Computing for Motion Recognition: Investigating the deployment of deep learning models for motion recognition on edge devices to reduce latency and improve privacy.
Interdisciplinary Applications: Exploring interdisciplinary applications, such as combining motion recognition with healthcare monitoring, robotics, and virtual reality.
Real-time and Low-latency Recognition: Designing efficient model architectures and hardware optimizations to achieve real-time or low-latency motion recognition suitable for interactive applications like gaming, augmented reality (AR), and robotics.
Interpretable and Explainable AI: Developing methods for explaining the decisions of deep learning models in motion recognition, enhancing transparency and interpretability for users and stakeholders in critical applications.
Benchmark Datasets and Evaluation Metrics: Creating standardized benchmark datasets and evaluation metrics tailored to specific application domains and types of human motions to enable fair comparisons and benchmarks.
Bias Mitigation and Fairness: Addressing issues related to bias and fairness in motion recognition systems, ensuring equitable performance across diverse demographic groups, and developing fair evaluation metrics.
Continual Learning for Long-term Recognition: Advancing continual learning techniques to adapt to evolving user behaviors and environmental conditions, ensuring consistent performance over extended periods.