A Survey on Video Anomaly Detection via Deep Learning: Human, Vehicle, and Environment

Video Anomaly Detection (VAD) has emerged as a pivotal task in computer vision, with broad relevance across multiple fields. Recent advances in deep learning have driven significant progress in this area, yet the field remains fragmented across domains and learning paradigms. This survey offers a comprehensive perspective on VAD, systematically organizing the literature across various supervision levels, as well as adaptive learning methods such as online, active, and continual learning. We examine the state of VAD across three major application categories: human-centric, vehicle-centric, and environment-centric scenarios, each with distinct challenges and design considerations. In doing so, we identify fundamental contributions and limitations of current methodologies. By consolidating insights from subfields, we aim to provide the community with a structured foundation for advancing both theoretical understanding and real-world applicability of VAD systems. This survey aims to support researchers by providing a useful reference, while also drawing attention to the broader set of open challenges in anomaly detection, including both fundamental research questions and practical obstacles to real-world deployment.

ALFred: An Active Learning Framework for Real-world Semi-supervised Anomaly Detection with Adaptive Thresholds

Video Anomaly Detection (VAD) can play a key role in spotting unusual activities in video footage. VAD is difficult to use in real-world settings due to the dynamic nature of human actions, environmental variations, and domain shifts. Traditional evaluation metrics often prove inadequate for such scenarios, as they rely on static assumptions and fall short of identifying a threshold that distinguishes normal from anomalous behavior in dynamic settings. To address this, we introduce an active learning framework tailored for VAD, designed for adapting to the ever-changing real-world conditions. Our approach leverages active learning to continuously select the most informative data points for labeling, thereby enhancing model adaptability. A critical innovation is the incorporation of a human-in-the-loop mechanism, which enables the identification of actual normal and anomalous instances from pseudo-labeling results generated by AI. This collected data allows the framework to define an adaptive threshold tailored to different environments, ensuring that the system remains effective as the definition of 'normal' shifts across various settings. Implemented within a lab-based framework that simulates real-world conditions, our approach allows rigorous testing and refinement of VAD algorithms with a new metric. Experimental results show that our method achieves an EBI (Error Balance Index) of 68.91 for Q3 in real-world simulated scenarios, demonstrating its practical effectiveness and significantly enhancing the applicability of VAD in dynamic environments.

MoCLIP: Motion-Aware Fine-Tuning and Distillation of CLIP for Human Motion Generation

Human motion generation is essential for fields such as animation, robotics, and virtual reality, requiring models that effectively capture motion dynamics from text descriptions. Existing approaches often rely on Contrastive Language-Image Pretraining (CLIP)-based text encoders, but their training on text-image pairs constrains their ability to understand temporal and kinematic structures inherent in motion and motion generation. This work introduces MoCLIP, a fine-tuned CLIP model with an additional motion encoding head, trained on motion sequences using contrastive learning and tethering loss. By explicitly incorporating motion-aware representations, MoCLIP enhances motion fidelity while remaining compatible with existing CLIP-based pipelines and seamlessly integrating into various CLIP-based methods. Experiments demonstrate that MoCLIP improves Top-1, Top-2, and Top-3 accuracy while maintaining competitive FID, leading to improved text-to-motion alignment results. These results highlight MoCLIP's versatility and effectiveness, establishing it as a robust framework for enhancing motion generation.

Shopformer: Transformer-Based Framework for Detecting Shoplifting via Human Pose

Shoplifting remains a costly issue for the retail sector, but traditional surveillance systems, which are mostly based on human monitoring, are still largely ineffective, with only about 2% of shoplifters being arrested. Existing AI-based approaches rely on pixel-level video analysis which raises privacy concerns, is sensitive to environmental variations, and demands significant computational resources. To address these limitations, we introduce Shopformer, a novel transformer-based model that detects shoplifting by analyzing pose sequences rather than raw video. We propose a custom tokenization strategy that converts pose sequences into compact embeddings for efficient transformer processing. To the best of our knowledge, this is the first pose-sequence-based transformer model for shoplifting detection. Evaluated on real-world pose data, our method outperforms state-of-the-art anomaly detection models, offering a privacy-preserving, and scalable solution for real-time retail surveillance. The code base for this work is available at https://github.com/TeCSAR-UNCC/Shopformer.

MoFM: A Large-Scale Human Motion Foundation Model

AFoundation Models (FM) have increasingly drawn the attention of researchers due to their scalability and generalization across diverse tasks. Inspired by the success of FMs and the principles that have driven advancements in Large Language Models (LLMs), we introduce MoFM as a novel Motion Foundation Model. MoFM is designed for the semantic understanding of complex human motions in both time and space. To facilitate large-scale training, MotionBook, a comprehensive human motion dictionary of discretized motions is designed and employed. MotionBook utilizes Thermal Cubes to capture spatio-temporal motion heatmaps, applying principles from discrete variational models to encode human movements into discrete units for a more efficient and scalable representation. MoFM, trained on a large corpus of motion data, provides a foundational backbone adaptable to diverse downstream tasks, supporting paradigms such as one-shot, unsupervised, and supervised tasks. This versatility makes MoFM well-suited for a wide range of motion-based applications.

Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World Shoplifting Dataset and Benchmark

Shoplifting poses a significant challenge for retailers, resulting in billions of dollars in annual losses. Traditional security measures often fall short, highlighting the need for intelligent solutions capable of detecting shoplifting behaviors in real time. This paper frames shoplifting detection as an anomaly detection problem, focusing on the identification of deviations from typical shopping patterns. We introduce PoseLift, a privacy-preserving dataset specifically designed for shoplifting detection, addressing challenges such as data scarcity, privacy concerns, and model biases. PoseLift is built in collaboration with a retail store and contains anonymized human pose data from real-world scenarios. By preserving essential behavioral information while anonymizing identities, PoseLift balances privacy and utility. We benchmark state-of-the-art pose-based anomaly detection models on this dataset, evaluating performance using a comprehensive set of metrics. Our results demonstrate that pose-based approaches achieve high detection accuracy while effectively addressing privacy and bias concerns inherent in traditional methods. As one of the first datasets capturing real-world shoplifting behaviors, PoseLift offers researchers a valuable tool to advance computer vision ethically and will be publicly available to foster innovation and collaboration. The dataset is available at https://github.com/TeCSAR-UNCC/PoseLift.

PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose Tokenization

Video Anomaly Detection (VAD) presents a significant challenge in computer vision, particularly due to the unpredictable and infrequent nature of anomalous events, coupled with the diverse and dynamic environments in which they occur. Human-centric VAD, a specialized area within this domain, faces additional complexities, including variations in human behavior, potential biases in data, and substantial privacy concerns related to human subjects. These issues complicate the development of models that are both robust and generalizable. To address these challenges, recent advancements have focused on pose-based VAD, which leverages human pose as a high-level feature to mitigate privacy concerns, reduce appearance biases, and minimize background interference. In this paper, we introduce PoseWatch, a novel transformer-based architecture designed specifically for human-centric pose-based VAD. PoseWatch features an innovative Spatio-Temporal Pose and Relative Pose (ST-PRP) tokenization method that enhances the representation of human motion over time, which is also beneficial for broader human behavior analysis tasks. The architecture's core, a Unified Encoder Twin Decoders (UETD) transformer, significantly improves the detection of anomalous behaviors in video data. Extensive evaluations across multiple benchmark datasets demonstrate that PoseWatch consistently outperforms existing methods, establishing a new state-of-the-art in pose-based VAD. This work not only demonstrates the efficacy of PoseWatch but also highlights the potential of integrating Natural Language Processing techniques with computer vision to advance human behavior analysis.

PHEVA: A Privacy-preserving Human-centric Video Anomaly Detection Dataset

PHEVA, a Privacy-preserving Human-centric Ethical Video Anomaly detection dataset. By removing pixel information and providing only de-identified human annotations, PHEVA safeguards personally identifiable information. The dataset includes seven indoor/outdoor scenes, featuring one novel, context-specific camera, and offers over 5x the pose-annotated frames compared to the largest previous dataset. This study benchmarks state-of-the-art methods on PHEVA using a comprehensive set of metrics, including the 10% Error Rate (10ER), a metric used for anomaly detection for the first time providing insights relevant to real-world deployment. As the first of its kind, PHEVA bridges the gap between conventional training and real-world deployment by introducing continual learning benchmarks, with models outperforming traditional methods in 82.14% of cases. The dataset is publicly available at https://github.com/TeCSAR-UNCC/PHEVA.git.

Evaluating the Effectiveness of Video Anomaly Detection in the Wild: Online Learning and Inference for Real-world Deployment

Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare. Tackling VAD in real-life settings poses significant challenges due to the dynamic nature of human actions, environmental variations, and domain shifts. Many research initiatives neglect these complexities, often concentrating on traditional testing methods that fail to account for performance on unseen datasets, creating a gap between theoretical models and their real-world utility. Online learning is a potential strategy to mitigate this issue by allowing models to adapt to new information continuously. This paper assesses how well current VAD algorithms can adjust to real-life conditions through an online learning framework, particularly those based on pose analysis, for their efficiency and privacy advantages. Our proposed framework enables continuous model updates with streaming data from novel environments, thus mirroring actual world challenges and evaluating the models' ability to adapt in real-time while maintaining accuracy. We investigate three state-of-the-art models in this setting, focusing on their adaptability across different domains. Our findings indicate that, even under the most challenging conditions, our online learning approach allows a model to preserve 89.39% of its original effectiveness compared to its offline-trained counterpart in a specific target domain.

Integrating AI into CCTV Systems: A Comprehensive Evaluation of Smart Video Surveillance in Community Space

This article presents an AI-enabled Smart Video Surveillance (SVS) designed to enhance safety in community spaces such as educational and recreational areas, and small businesses. The proposed system innovatively integrates with existing CCTV and wired camera networks, simplifying its adoption across various community cases to leverage recent AI advancements. Our SVS system, focusing on privacy, uses metadata instead of pixel data for activity recognition, aligning with ethical standards. It features cloud-based infrastructure and a mobile app for real-time, privacy-conscious alerts in communities. This article notably pioneers a comprehensive real-world evaluation of the SVS system, covering AI-driven visual processing, statistical analysis, database management, cloud communication, and user notifications. It's also the first to assess an end-to-end anomaly detection system's performance, vital for identifying potential public safety incidents. For our evaluation, we implemented the system in a community college, serving as an ideal model to exemplify the proposed system's capabilities. Our findings in this setting demonstrate the system's robustness, with throughput, latency, and scalability effectively managing 16 CCTV cameras. The system maintained a consistent 16.5 frames per second (FPS) over a 21-hour operation. The average end-to-end latency for detecting behavioral anomalies and alerting users was 26.76 seconds.