| Abstract: | Two crucial aspects of general-purpose embedded visual point tracking are addressed in this paper. First, the algorithm should reliably track as many points as possible. Second, the computation should achieve real-time video processing, which is challenging on low power embedded platforms. We propose a new multi-scale semi-dense point tracker called Video Extruder, whose purpose is to fill the gap between short-term, dense motion estimation (optical flow) and long-term, sparse salient point tracking. This paper presents a new detector, including a new salience function with low computational complexity and a new selection strategy that allows to obtain a large number of keypoints. Its density and reliability in mobile video scenarios are compared with those of the FAST detector. Then, a multi-scale matching strategy is presented, based on hybrid regional coarse-to-fine and temporal prediction, which provides robustness to large camera and object accelerations. Filtering and merging strategies are then used to eliminate most of the wrong or useless trajectories. Thanks to its high degree of parallelism, the proposed algorithm extracts beams of trajectories from the video very efficiently. We compare it with the state-of-the-art pyramidal Lucas–Kanade point tracker and show that, in short range mobile video scenarios, it yields similar quality results, while being up to one order of magnitude faster. Three different parallel implementations of this tracker are presented, on multi-core CPU, GPU and ARM SoCs. On a commodity 2010 CPU, it can track 8,500 points in a 640 × 480 video at 150 Hz. |
