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

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 the condition of no access to the original data. The results show that this method can be effectively used for tactical assassination warning, suspicious object screening and environmental drastic change monitoring with strong deployability and tactical interpretation value. The study emphasizes the importance of color features as low semantic battlefield signal carriers, and its battlefield intelligent perception capability will be further extended by combining graph neural networks and temporal modeling in the future.