Explainable Video Anomaly Detection

• Explainable Video Anomaly Detection is a field that develops algorithms to detect abnormal events in videos while providing human-interpretable explanations, ensuring trust and regulatory compliance.
• Techniques combine object-level detection, multimodal vision-language models, and instance tracking to accurately localize and justify anomalies in dynamic scenes.
• Recent advances improve detection accuracy and spatial grounding while addressing challenges like computational cost and context modeling in safety-critical applications.

Explainable Video Anomaly Detection (VAD) is the study and development of algorithms that not only localize or identify abnormal spatiotemporal events in video streams but also generate human-interpretable explanations justifying these anomaly decisions. With applications in surveillance, safety-critical infrastructure, and assistive technologies, explainability is essential for trust, actionable response, and regulatory compliance. Recent progress in this field leverages vision-LLMs (VLMs), LLMs, multimodal LLMs (MLLMs), and modular embedding architectures to support granular, context-aware, and semantically explicit explanations alongside state-of-the-art detection performance.

1. Foundations and Motivations

The core objective of explainable VAD is to address two longstanding gaps in conventional anomaly detection: (1) robustly detecting anomalies not limited to a finite set of object or activity classes, and (2) producing explanations that map system decisions to interpretable, verifiable evidence. Traditional VAD methods—whether prediction-based, reconstruction-based, or feature-space modeling—achieve high accuracy but reduce the rationale to opaque anomaly scores, lacking transparency concerning which scene element or behavior triggered the alert (Baradaran et al., 2022, Singh et al., 2022, Szymanowicz et al., 2021). Misinterpretation of events, incomplete context modeling, and ungrounded explanations are especially problematic in dynamic, open-world, or interaction-heavy scenes.

The explainability goal is twofold: generate rationales codified in high-level, human-readable language (textual captions, attribute lists), and spatially/temporally ground these rationales to precise instances, objects, or regions in the video—a requirement especially acute in safety-critical domains (Song et al., 13 Jan 2026). Recent advances operationalize these aims through object-level modeling, multimodal language-image architectures, and attention-based or exemplar-based strategies (Mumcu et al., 16 Oct 2025, Ding et al., 2024).

2. Key Methodological Paradigms

Object- and Instance-Level Explainability

Many recent approaches decompose VAD into object-centric or instance-tracking subproblems, enabling explanations to reference not just the “scene-level” anomaly but “who did what, to whom, where, and when.” Instance-aligned pipelines employ dedicated object detectors, tracking modules (e.g., ByteTrack), appearance and motion encoders, and segmentation modules (e.g., SAM2) to localize and uniquely label each entity contributing to an anomalous event (Song et al., 13 Jan 2026). Captions are generated in a structured form, often partitioned into “appearance” and “motion” clauses, and claims are linked to specific segmentation masks to guarantee spatial grounding and verifiability.

Object-aware branches explicitly translate RGB frames into semantic segmentation maps and motion fields. Disagreement between predicted and ground-truth segmentation (or motion) exposes what object types, positions, or kinetics are outside the learned model of normality, and overlay heatmaps visually indicate which objects or pixels are flagged (Baradaran et al., 2022). This pipeline ensures that explanations not only identify the presence of an anomaly but point to the exact cause (e.g., an unknown vehicle type in a forbidden region, or an established class moving with abnormal speed).

Multimodal LLM-Based Explainability

Models leveraging VLMs, LLMs, or MLLMs ground their reasoning in natural-language generation. Approaches such as MLLM-EVAD shift from frame-level detection to interaction-level modeling, using object-centric crops, temporal pairings, and multimodal prompts to query an MLLM for concise activity or interaction descriptions (Mumcu et al., 16 Oct 2025). Textual exemplars from nominal (non-anomalous) video constitute the reference set; test-time explanations are formulated as nearest-neighbor comparisons in the sentence embedding space, exposing the semantic deviation responsible for the anomaly score.

A closely related direction exemplified by VERA treats the prompt itself—the set of “guiding questions” describing facets of normal and abnormal patterns—as a learnable parameter. Iterative, data-driven verbalized optimization yields a prompt suite that, when applied to test clips, enables the VLM to detect and articulate the specific type of deviation, directly referencing which guiding criterion has been breached (Ye et al., 2024).

Table: Core Explainability Strategies

Model/Framework	Explanation Mechanism	Level of Grounding
MLLM-EVAD (Mumcu et al., 16 Oct 2025)	MLLM-generated activity/interactions, nearest-exemplar text	Crop/object-interaction
Instance-Aligned (Song et al., 13 Jan 2026)	Object-specific captions + mask grounding	Instance/pixel
VERA (Ye et al., 2024)	Prompted VLM with learned guiding questions	Clip/segment, textual facets
Object-Class Image Translation (Baradaran et al., 2022)	Semantic/motion map deviation, visual overlay	Pixel/object

3. Model Architectures and Pipelines

Multistage Explainable VAD Pipelines

Explainable VAD solutions are typically modular. The major stages may include:

1. Frame/Object Sampling & Detection: Raw frames are batch-processed by standard object detectors (e.g., Detectron2) and multi-object trackers (e.g., ByteTrack) to extract tracklets or ROI volumes.
2. Region-of-Interest Cropping: For either singletons or interacting object pairs, temporal pairs of spatially aligned crops are generated (spacing Δ equal to 1 second is common) (Mumcu et al., 16 Oct 2025).
3. Vision-Language Inference: Each crop (or spatiotemporal segment) is encoded and used as input for a VLM/MLLM, which responds to a system/user prompt by generating a brief textual rationale (often one sentence), describing the activities and potential interactions.
4. Text Embedding and Scoring: Descriptions are embedded (e.g., by Sentence-BERT) and compared against a database of nominal exemplars using cosine similarity. The anomaly score reflects semantic distance: larger discrepancies correspond to higher anomaly scores, and the direction of deviation is explained by the mismatched captions.
5. Exemplars and Thresholding: During model construction, embedding space exemplars are greedily selected to cover the nominal description distribution, omitting redundant samples (Mumcu et al., 16 Oct 2025).
6. Rationale Generation: At detection time, flagged anomalies are explained by displaying the generated description and (optionally) the closest nominal exemplar for comparative context.

Alternative pipelines, such as SlowFastVAD (Ding et al., 14 Apr 2025), deploy a fast path (real-time reconstruction-error DNN) and a slow path (RAG-augmented VLM). Only ambiguous or uncertain segments are subject to slow, knowledge-based chain-of-thought reasoning, which retrieves scene-specific rules and produces explicit, rule-citing explanations, supporting both computational efficiency and interpretability.

Hierarchical and Event-Based Reasoning

Recent frameworks such as VADTree (Li et al., 26 Oct 2025) address variable-length and overlapping anomalies by decomposing the video into a hierarchical granularity-aware tree, with nodes corresponding to generic event segments. An LLM is prompted with node-specific segment descriptions, augmented by multi-dimensional priors (scene type, object type, activity), generating both node-wise anomaly scores and step-by-step textual rationales. Intra- and inter-cluster refinements ensure local semantic consistency and multi-scale interpretability.

Long-term context modules (e.g., in VAD-LLaMA (Lv et al., 2024)) further enrich detection by maintaining memory banks of normal and abnormal clip features, fusing this history via attention mechanisms into the anomaly judgment, and prompting a VLLM to produce both time-localized detection and a detailed textual justification, circumventing the pitfalls of brittle anomaly-threshold selection.

4. Quantitative and Qualitative Results

Explainable VAD methods systematically report standard region-based detection criterion (RBDC), track-based detection criterion (TBDC), and frame-level AUC on benchmarks such as ComplexVAD, Street Scene, CUHK Avenue, UCF-Crime, XD-Violence, and SHanghaiTech (Mumcu et al., 16 Oct 2025, Singh et al., 2022, Ding et al., 2024). MLLM-EVAD achieved AUC improvements of 5–6 points over prior scene-graph-based and interaction-unaware methods on ComplexVAD (RBDC: 24.0 vs. 19.0; TBDC: 68.0 vs. 64.0), and maintained or improved upon SOTA on non-interaction datasets (Street Scene, Avenue).

Ablation studies demonstrate that semantic similarity in the sentence embedding space is more effective and less brittle than lexical scores (BLEU/METEOR), and changing MLLM backbones (GPT-4o to Gemma 3) can yield significant gains in interaction-heavy scenes (Mumcu et al., 16 Oct 2025). For instance-level pipelines, high spatial alignment scores (IoU) and caption-to-mask F_SC scores directly quantify explanation quality (Song et al., 13 Jan 2026). Training-free VLM approaches such as VERA match or exceed the AUC of fine-tuned or MIL-based SOTA, while offering prompt-level explanations (Ye et al., 2024). Efficiency, cross-dataset generalization, and explanation fidelity are increasingly quantified through joint segmentation-caption metrics, role-based captioning accuracy, and entity false positive rates.

Qualitative reports across the literature highlight the interpretability of explanation mechanisms—for example, contrasting the test event “person is crouching down on the ground” (flagged as unseen) with the nearest nominal “person is walking across the grass,” or stepwise chain-of-thought treatments citing knowledge-base rules in SlowFastVAD (Ding et al., 14 Apr 2025).

5. Taxonomy of Explainable VAD Approaches

A comprehensive review (Ding et al., 2024) categorizes current explainable VAD methods along four axes:

Paradigm	Representative Methods	Key Mechanisms
Fine-tuning-based	VADor, Holmes-VAD, VLAVAD, STPrompt	Model/LLM pretraining + tuning
Prompt-based (training-free)	LAVAD, ALFA, VERA	Frozen VLM, question prompts
Few-/Zero-shot domain adaptation	AnomalyRuler, STPrompt	Rule-based/LLM domain transfer
Open-world/class-agnostic	OVVAD, CALLM, Holmes-VAU	Dual-class heads, pseudo-labelling

Models differ in their reliance on training data, architectural complexity, and generalizability to novel anomaly types or long-tail distributions. Prompt-based training-free models offer strong scalability, while fine-tuned, instruction-tuned, or hierarchy-based models exploit richer context at the expense of supervision or compute.

6. Limitations and Open Problems

Despite advances, key limitations remain:

• Compute/Latency: State-of-the-art MLLM- and VLM-based models incur high computational cost and inference latency, limiting real-time deployment. There is ongoing work toward lightweight or scene-adaptive vision-language modules, and pipeline optimizations such as selective slow-path invocation (Mumcu et al., 16 Oct 2025, Ding et al., 14 Apr 2025).
• Granular and Verifiable Explanations: Many methods still output scene or frame-level rationales lacking evidential grounding in object instances or regions, which is problematic for interaction or multi-agent scenarios. Instance-aligned captioning (Song et al., 13 Jan 2026) exposes significant deficiencies in spatial alignment of explanations, especially for victims/targets.
• Explainability Benchmarks: There is a dearth of large-scale, scene-specific datasets with fine-grained, instance-aligned textual annotations. Datasets such as VIEW360+ (Song et al., 13 Jan 2026) and VAD-Instruct50k (Zhang et al., 2024) begin to fill this gap.
• Open-Vocabulary and Out-of-Distribution: The robustness of current explainable VAD pipelines to out-of-distribution anomalies, novel object types, and unseen interaction patterns remains limited. Open-world and dual-head detection are active research directions (Ding et al., 2024, Mumcu et al., 16 Oct 2025).
• Long-term Temporal Reasoning: Scalability to long video streams and dynamic context reasoning (e.g., maintaining event memory) often face architectural or token-length bottlenecks (Lv et al., 2024, Li et al., 26 Oct 2025).

7. Future Directions

Several axes for development are prominent:

• Hybrid Pipelines: Combining high-precision, fine-tuned methods with training-free prompt approaches for both accuracy and efficiency.
• Hierarchical and Interactive Explainability: Multi-scale, event-level reasoning and interactive explanation refinement, leveraging user feedback or counterfactual query generation (Li et al., 26 Oct 2025, Ding et al., 2024).
• Open-Vocabulary Detection: Integrating LLM-guided open-set detectors (e.g., Grounding DINO, Yolo-World) for object type generality (Mumcu et al., 16 Oct 2025).
• Dataset and Evaluation Expansion: Crowdsourcing, semi-automatic annotation, and benchmarking explainability via joint text–mask quality, role disambiguation, and scenario completeness (Song et al., 13 Jan 2026).
• Efficient Deployment: Pursuing lightweight, scene-adaptive VLM/LLM architectures and streaming inference mechanisms for practical, high-scale monitoring environments.

In summary, explainable video anomaly detection has matured from heuristic rationales to systematically modular and multimodal architectures. By fusing object-centric tracking, vision-language generation, textual-semantic scoring, and instance-aligned spatial grounding, these methods simultaneously advance both detection accuracy and actionable transparency across diverse and challenging real-world benchmarks (Mumcu et al., 16 Oct 2025, Ding et al., 2024, Song et al., 13 Jan 2026).