Unsupervised Detection of Anomalous Human Actions Using Normalizing Flows

Główne pojęcia

The core message of this paper is to introduce a new task called human action anomaly detection (HAAD) that aims to identify anomalous motions in an unsupervised manner given only the pre-determined normal category of training action samples. The authors propose a normalizing flow (NF)-based detection framework that leverages the sample likelihood to indicate anomalies, and incorporate extra encoding streams for finer modeling of body subsets to jointly discover global and local motion anomalies.

Streszczenie

The paper introduces the task of human action anomaly detection (HAAD), which differs from previous human activity anomaly detection tasks by focusing on identifying anomalous motions with respect to a specific normal action category. To address this task, the authors propose a novel NF-based framework with the following key components:

Frequency-guided encoding: The authors apply Discrete Cosine Transform (DCT) to the input motion data to convert it from the temporal to the frequency domain, which helps mitigate the issue of data instability caused by jittery motion.

Multi-level action feature learning: In addition to learning full-body motion features, the authors incorporate extra encoding streams to model the motion of body subsets (upper and lower body) separately. This allows the framework to jointly discover global and local motion anomalies.

Normalizing flow for anomaly detection: The authors use an NF model to learn the likelihood of normal action samples, and then employ a K-nearest neighbor (KNN)-based scoring approach to compute the anomaly score for test samples.

Extensive experiments on two human action datasets demonstrate that the proposed method outperforms baseline approaches adapted from state-of-the-art human activity anomaly detection techniques.

Statystyki

The recorded human motion data can contain different degrees of noise, including jittering or flipping.
Anomalies in human action can often occur in specific body parts, rather than the full body.

Cytaty

"Compared to conventional semantic-free AD, introducing specific action labels for AD requires to model the underlying semantic features of the given action type."
"Since the recorded 3D human motion can include noise during recording, the motion data can suffer from different degrees of jitter that hinders detection accuracy."
"We notice that human motion can only involve local differences in some key body parts to perform different actions."

Kluczowe wnioski z

Frequency-Guided Multi-Level Human Action Anomaly Detection with Normalizing Flows

by Shun Maeda,C... o arxiv.org 04-29-2024

https://arxiv.org/pdf/2404.17381.pdf

Głębsze pytania

How can the proposed multi-level action feature learning be further extended to adaptively weight the contributions of different body subsets based on the specific action type

The proposed multi-level action feature learning can be further extended to adaptively weight the contributions of different body subsets based on the specific action type by incorporating a mechanism that dynamically adjusts the importance of each body subset depending on the characteristics of the action being analyzed. This adaptive weighting can be achieved by introducing attention mechanisms or learnable gating mechanisms within the model architecture.
For example, an attention mechanism can be employed to assign different weights to the features extracted from different body subsets based on their relevance to the specific action category under consideration. By attending more to the body subsets that are more informative for a particular action and less to those that are less relevant, the model can adaptively focus on the most discriminative features for each action type.
Similarly, a learnable gating mechanism can be introduced to modulate the contributions of different body subsets dynamically during the learning process. By learning how to gate or scale the features from different body subsets based on the input data, the model can effectively adjust the importance of each subset for different actions, leading to more adaptive and context-aware feature learning.

Can the number of DCT coefficients be learned in an adaptive manner during the training process to better handle the varying degrees of motion instability across different action categories

To better handle the varying degrees of motion instability across different action categories, the number of DCT coefficients can be learned in an adaptive manner during the training process. This adaptive learning approach can involve incorporating a hyperparameter optimization strategy or a learnable mechanism within the model to dynamically adjust the number of DCT coefficients based on the characteristics of the input data and the complexity of the action being analyzed.
One approach could be to introduce a hyperparameter optimization algorithm, such as Bayesian optimization or grid search, to search for the optimal number of DCT coefficients that maximizes the detection performance for each action category. By iteratively evaluating the model performance with different numbers of coefficients and selecting the configuration that yields the best results, the model can adaptively determine the most suitable number of coefficients for each action type.
Alternatively, a learnable mechanism can be integrated into the model architecture to dynamically adjust the number of DCT coefficients based on the input data characteristics. This mechanism can be trained to predict the optimal number of coefficients for each action category during the training process, allowing the model to automatically adapt to the varying degrees of motion instability and complexity across different actions.

What other types of human-centric tasks beyond anomaly detection could benefit from the semantic-aware modeling approach introduced in this work

The semantic-aware modeling approach introduced in this work can benefit various other human-centric tasks beyond anomaly detection, particularly those that require understanding and interpreting human actions in a meaningful way. Some potential applications include:

Human Action Recognition: The semantic-aware modeling can enhance the performance of human action recognition tasks by enabling the model to capture the underlying semantic features of different action types. By incorporating specific action labels and focusing on distinguishing spatial-temporally analogous actions, the model can achieve more accurate and robust action classification.

Behavior Analysis in Surveillance: In surveillance systems, the semantic-aware modeling can be utilized to analyze human behaviors and detect suspicious or abnormal activities. By incorporating specific semantic labels for different behaviors, the model can effectively identify unusual or potentially harmful actions in real-time surveillance footage.

Healthcare Monitoring: In healthcare applications, the semantic-aware modeling can be applied to monitor and analyze human movements for assessing physical activities, detecting anomalies in movement patterns, and providing personalized feedback for rehabilitation or physical therapy programs.

Sports Performance Analysis: The approach can be used in sports analytics to analyze and interpret human movements in various sports activities. By understanding the semantic context of different sports actions, the model can provide insights into performance optimization, injury prevention, and skill enhancement for athletes.

Overall, the semantic-aware modeling approach can be valuable in a wide range of human-centric tasks that involve understanding, interpreting, and analyzing human actions in different contexts.