  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.
