Multiple feature fusion-based video face tracking for IoT big data

TL;DR

This paper introduces a multi-feature fusion algorithm combining integral histograms and particle filtering for real-time, robust video face tracking in IoT applications, enhancing accuracy and efficiency.

Contribution

It proposes a novel multi-feature fusion method with adaptive weighting and real-time window adjustment, improving face tracking stability and robustness in IoT scenarios.

Findings

Enhanced video tracking accuracy

Simplified particle filtering calculations

Improved speed and robustness

Abstract

With the advancement of IoT and artificial intelligence technologies, and the need for rapid application growth in fields such as security entrance control and financial business trade, facial information processing has become an important means for achieving identity authentication and information security. In this paper, we propose a multi-feature fusion algorithm based on integral histograms and a real-time update tracking particle filtering module. First, edge and colour features are extracted, weighting methods are used to weight the colour histogram and edge features to describe facial features, and fusion of colour and edge features is made adaptive by using fusion coefficients to improve face tracking reliability. Then, the integral histogram is integrated into the particle filtering algorithm to simplify the calculation steps of complex particles. Finally, the tracking window size is adjusted in real time according to the change in the average distance from the particle centre to the edge of the current model and the initial model to reduce the drift problem and achieve stable tracking with significant changes in the target dimension. The results show that the algorithm improves video tracking accuracy, simplifies particle operation complexity, improves the speed, and has good anti-interference ability and robustness.