WSCIF: A Weakly-Supervised Color Intelligence Framework for Tactical Anomaly Detection in Surveillance Keyframes
Wei Meng

TL;DR
This paper introduces a lightweight, color-based anomaly detection framework for surveillance videos in high-risk environments, effectively identifying threats without needing labeled data.
Contribution
It presents a novel weakly-supervised method combining KMeans clustering and RGB histograms for tactical anomaly detection in resource-constrained scenarios.
Findings
Successfully detects anomalous frames related to threats in real surveillance footage
Effective in identifying high-energy light sources and reflective interference
Demonstrates strong deployability and tactical interpretability
Abstract
The deployment of traditional deep learning models in high-risk security tasks in an unlabeled, data-non-exploitable video intelligence environment faces significant challenges. In this paper, we propose a lightweight anomaly detection framework based on color features for surveillance video clips in a high sensitivity tactical mission, aiming to quickly identify and interpret potential threat events under resource-constrained and data-sensitive conditions. The method fuses unsupervised KMeans clustering with RGB channel histogram modeling to achieve composite detection of structural anomalies and color mutation signals in key frames. The experiment takes an operation surveillance video occurring in an African country as a research sample, and successfully identifies multiple highly anomalous frames related to high-energy light sources, target presence, and reflective interference under…
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Taxonomy
TopicsNetwork Security and Intrusion Detection · Anomaly Detection Techniques and Applications
