Video Anomaly Retrieval: New Benchmarks and Model

المفاهيم الأساسية

The author proposes Video Anomaly Retrieval (VAR) as a new task to retrieve relevant anomalous videos using detailed descriptions, introducing large-scale benchmarks and a model called ALAN for VAR.

الملخص

The content discusses the importance of Video Anomaly Retrieval (VAR) in bridging the gap between literature and real-world applications. It introduces benchmarks UCFCrime-AR and XDViolence-AR, along with the ALAN model for VAR. The method involves anomaly-led sampling, video prompt-based masked phrase modeling, and cross-modal alignment.
The article highlights the challenges of VAR compared to traditional video retrieval methods due to long untrimmed videos. It explains the structure of ALAN, including encoders for video, text, and audio, as well as the anomaly-led sampling mechanism. The VPMPM pretext task is introduced for fine-grained associations in video-text retrieval. Additionally, cross-modal alignment techniques are discussed to match representations from different modalities.
Key metrics or figures used include R@K values for evaluation metrics on UCFCrime-AR and XDViolence-AR. Ablation studies on anomaly-led sampling, VPMPM, and cross-modal alignment are conducted to analyze their impact on performance. The influence of hyperparameters like α is also explored.
The qualitative analysis includes visualization of retrieval results on UCFCrime-AR and coarse caption retrieval performance with different lengths of captions.

الإحصائيات

Videos possess space-time information.
UCF-Crime dataset consists of 1900 untrimmed videos.
XD-Violence dataset contains 3954 long videos.
Average length of videos in UCFCrime-AR is 242s.
Average length of videos in XDViolence-AR is 164s.

اقتباسات

"In reality, users tend to search a specific video rather than a series of approximate videos."
"Our VAR is considerably different from traditional video retrieval."
"Such a setup more meets realistic requirements."

الرؤى الأساسية المستخلصة من

Towards Video Anomaly Retrieval from Video Anomaly Detection

by Peng Wu,Jing... في arxiv.org 02-29-2024

https://arxiv.org/pdf/2307.12545.pdf

استفسارات أعمق

How does the proposed VAR task address limitations in current anomaly detection methods

The proposed Video Anomaly Retrieval (VAR) task addresses limitations in current anomaly detection methods by focusing on retrieving relevant anomalous videos using detailed descriptions, such as text captions and synchronous audios. Unlike traditional anomaly detection methods that primarily focus on online detecting anomalies through binary or multiple event classification, VAR aims to bridge the gap between literature and real-world applications by providing a more practical approach to identifying anomalous events in videos. By incorporating cross-modal retrieval techniques, VAR allows for a more comprehensive characterization of sequential events depicted in videos, addressing the limitation of single labels being insufficient to explain complex interactions between actions and entities over time.

What potential applications can benefit most from Video Anomaly Retrieval (VAR)

Video Anomaly Retrieval (VAR) has the potential to benefit various applications across different industries. Some key areas that can benefit most from VAR include:

Surveillance Systems: In smart ground and car surveillance systems, VAR can help security personnel quickly identify specific video segments containing anomalous activities based on detailed descriptions provided.
Law Enforcement: Law enforcement agencies can use VAR to search for relevant video footage related to criminal activities described in textual or audio form, aiding investigations and evidence collection.
Retail Security: Retail stores can utilize VAR for monitoring suspicious behavior or theft incidents captured on surveillance cameras by searching for specific events described in text or audio queries.
Traffic Monitoring: Traffic management authorities can leverage VAR to retrieve videos depicting accidents or traffic violations based on detailed descriptions provided through text captions or audio recordings.

How might advancements in cross-modal alignment impact other fields beyond video analysis

Advancements in cross-modal alignment resulting from research into Video Anomaly Retrieval (VAR) have implications beyond video analysis:

Medical Imaging: Cross-modal alignment techniques developed for VAR could be applied to medical imaging data where images need to be aligned with patient records or diagnostic reports for accurate analysis.
Natural Language Processing (NLP): Improved cross-modal alignment methods could enhance multimodal NLP tasks like image captioning where images need to be accurately described based on textual inputs.
Autonomous Vehicles: In autonomous driving systems, advancements in cross-modal alignment could aid in aligning sensor data with environmental cues captured through cameras and other sensors for better decision-making algorithms.
E-commerce Recommendation Systems: Cross-modal alignment techniques from VAR research could improve recommendation systems by aligning product images with user preferences expressed through text queries.

These advancements have the potential to enhance various fields by enabling better integration of information from different modalities leading to improved performance and accuracy in diverse applications beyond video analysis alone.