Learning Human Action Recognition Representations Without Real Humans

Pre-training on massive video datasets has become essential to achieve high action recognition performance on smaller downstream datasets. However, most large-scale video datasets contain images of people and hence are accompanied with issues related to privacy, ethics, and data protection, often preventing them from being publicly shared for reproducible research. Existing work has attempted to alleviate these problems by blurring faces, downsampling videos, or training on synthetic data. On the other hand, analysis on the transferability of privacy-preserving pre-trained models to downstream tasks has been limited. In this work, we study this problem by first asking the question: can we pre-train models for human action recognition with data that does not include real humans? To this end, we present, for the first time, a benchmark that leverages real-world videos with humans removed and synthetic data containing virtual humans to pre-train a model. We then evaluate the transferability of the representation learned on this data to a diverse set of downstream action recognition benchmarks. Furthermore, we propose a novel pre-training strategy, called Privacy-Preserving MAE-Align, to effectively combine synthetic data and human-removed real data. Our approach outperforms previous baselines by up to 5% and closes the performance gap between human and no-human action recognition representations on downstream tasks, for both linear probing and fine-tuning. Our benchmark, code, and models are available at https://github.com/howardzh01/PPMA .Comment: 19 pages, 7 figures, 2023 NeurIPS Datasets and Benchmarks Trac

DIY Human Action Data Set Generation

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing data set, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition data sets, that this new data generation technique can improve the performance of current action recognition neural nets

Efficient and effective human action recognition in video through motion boundary description with a compact set of trajectories

Human action recognition (HAR) is at the core of human-computer interaction and video scene understanding. However, achieving effective HAR in an unconstrained environment is still a challenging task. To that end, trajectory-based video representations are currently widely used. Despite the promising levels of effectiveness achieved by these approaches, problems regarding computational complexity and the presence of redundant trajectories still need to be addressed in a satisfactory way. In this paper, we propose a method for trajectory rejection, reducing the number of redundant trajectories without degrading the effectiveness of HAR. Furthermore, to realize efficient optical flow estimation prior to trajectory extraction, we integrate a method for dynamic frame skipping. Experiments with four publicly available human action datasets show that the proposed approach outperforms state-of-the-art HAR approaches in terms of effectiveness, while simultaneously mitigating the computational complexity

Human and Animal Behavior Understanding

Human and animal behavior understanding is an important yet challenging task in computer vision. It has a variety of real-world applications including human computer interaction (HCI), video surveillance, pharmacology, genetics, etc. We first present an evaluation of spatiotemporal interest point features (STIPs) for depth-based human action recognition, and then propose a framework call TriViews for 3D human action recognition with RGB-D data. Finally, we investigate a new approach for animal behavior recognition based on tracking, video content extraction and data fusion.;STIPs features are widely used with good performance for action recognition using the visible light videos. Recently, with the advance of depth imaging technology, a new modality has appeared for human action recognition. It is important to assess the performance and usefulness of the STIPs features for action analysis on the new modality of 3D depth map. Three detectors and six descriptors are combined to form various STIPs features in this thesis. Experiments are conducted on four challenging depth datasets.;We present an effective framework called TriViews to utilize 3D information for human action recognition. It projects the 3D depth maps into three views, i.e., front, side, and top views. Under this framework, five features are extracted from each view, separately. Then the three views are combined to derive a complete description of the 3D data. The five features characterize action patterns from different aspects, among which the top three best features are selected and fused based on a probabilistic fusion approach (PFA). We evaluate the proposed framework on three challenging depth action datasets. The experimental results show that the proposed TriViews framework achieves the most accurate results for depth-based action recognition, better than the state-of-the-art methods on all three databases.;Compared to human actions, animal behaviors exhibit some different characteristics. For example, animal body is much less expressive than human body, so some visual features and frameworks which are widely used for human action representation, cannot work well for animals. We investigate two features for mice behavior recognition, i.e., sparse and dense trajectory features. Sparse trajectory feature relies on tracking heavily. If tracking fails, the performance of sparse trajectory feature may deteriorate. In contrast, dense trajectory features are much more robust without relying on the tracking, thus the integration of these two features could be of practical significance. A fusion approach is proposed for mice behavior recognition. Experimental results on two public databases show that the integration of sparse and dense trajectory features can improve the recognition performance

Episodic Reasoning for Vision-Based Human Action Recognition

Smart Spaces, Ambient Intelligence, and Ambient Assisted Living are environmental paradigms that strongly depend on their capability to recognize human actions. While most solutions rest on sensor value interpretations and video analysis applications, few have realized the importance of incorporating common-sense capabilities to support the recognition process. Unfortunately, human action recognition cannot be successfully accomplished by only analyzing body postures. On the contrary, this task should be supported by profound knowledge of human agency nature and its tight connection to the reasons and motivations that explain it. The combination of this knowledge and the knowledge about how the world works is essential for recognizing and understanding human actions without committing common-senseless mistakes. This work demonstrates the impact that episodic reasoning has in improving the accuracy of a computer vision system for human action recognition. This work also presents formalization, implementation, and evaluation details of the knowledge model that supports the episodic reasoning