Detection-Tracking for Efficient Person Analysis: The DetTA Pipeline

TL;DR

The paper presents DetTA, a modular detection-tracking-analysis pipeline for robots that improves person attribute analysis efficiency and runtime by integrating tracking with analysis modules and employing a 'free-flight' mode for prediction-based analysis.

Contribution

It introduces a fully modular pipeline combining detection, tracking, and analysis, and demonstrates the benefits of temporal filtering and a 'free-flight' mode for efficiency and stability.

Findings

Slight improvement in analysis accuracy with integrated tracking.

Significant runtime boost using 'free-flight' mode.

Stable prediction quality despite reduced analysis frequency.

Abstract

In the past decade many robots were deployed in the wild, and people detection and tracking is an important component of such deployments. On top of that, one often needs to run modules which analyze persons and extract higher level attributes such as age and gender, or dynamic information like gaze and pose. The latter ones are especially necessary for building a reactive, social robot-person interaction. In this paper, we combine those components in a fully modular detection-tracking-analysis pipeline, called DetTA. We investigate the benefits of such an integration on the example of head and skeleton pose, by using the consistent track ID for a temporal filtering of the analysis modules' observations, showing a slight improvement in a challenging real-world scenario. We also study the potential of a so-called "free-flight" mode, where the analysis of a person attribute only relies on the filter's predictions for certain frames. Here, our study shows that this boosts the runtime dramatically, while the prediction quality remains stable. This insight is especially important for reducing power consumption and sharing precious (GPU-)memory when running many analysis components on a mobile platform, especially so in the era of expensive deep learning methods.