Fast Task-Specific Target Detection via Graph Based Constraints Representation and Checking

TL;DR

This paper introduces a rapid, task-specific target detection framework for robotics that employs early recognition and a graph-based constraint checking policy to improve speed and reliability, validated across multiple scenarios.

Contribution

It proposes a novel approach combining early recognition with a graph-based policy for efficient target detection, including a bounded-time sub-optimal checking sequence.

Findings

Effective in rigid object detection scenarios

Validates non-rigid body part detection in real-world robotics

Demonstrates applicability in human-robot interaction systems

Abstract

In this work, we present a fast target detection framework for real-world robotics applications. Considering that an intelligent agent attends to a task-specific object target during execution, our goal is to detect the object efficiently. We propose the concept of early recognition, which influences the candidate proposal process to achieve fast and reliable detection performance. To check the target constraints efficiently, we put forward a novel policy to generate a sub-optimal checking order, and prove that it has bounded time cost compared to the optimal checking sequence, which is not achievable in polynomial time. Experiments on two different scenarios: 1) rigid object and 2) non-rigid body part detection validate our pipeline. To show that our method is widely applicable, we further present a human-robot interaction system based on our non-rigid body part detection.