  In this paper, we propose a data-model-hardware tri-design framework for
high-throughput, low-cost, and high-accuracy multi-object tracking (MOT) on
High-Definition (HD) video stream. First, to enable ultra-light video
intelligence, we propose temporal frame-filtering and spatial saliency-focusing
approaches to reduce the complexity of massive video data. Second, we exploit
structure-aware weight sparsity to design a hardware-friendly model compression
method. Third, assisted with data and model complexity reduction, we propose a
sparsity-aware, scalable, and low-power accelerator design, aiming to deliver
real-time performance with high energy efficiency. Different from existing
works, we make a solid step towards the synergized software/hardware
co-optimization for realistic MOT model implementation. Compared to the
state-of-the-art MOT baseline, our tri-design approach can achieve 12.5x
latency reduction, 20.9x effective frame rate improvement, 5.83x lower power,
and 9.78x better energy efficiency, without much accuracy drop.
