It is a challenging task to develop an effective multi-target tracking algorithm for camera networks due to the factors, such as spurious measurement, limited field of view, complexity of data association algorithm, and so on. In a real-life environment, the system model for camera networks is usually nonlinear, and hence, a Kalman filter may be inappropriate for modeling this system. Besides, the main drawback of traditional joint probabilistic data association (JPDA) is prone to raise the combinatorial explosion problem when the association probabilities have to be calculated. To solve these problems, a multi-target square-root cubature information weighted consensus filter (MTSCF) combined with a K-best joint probabilistic data association algorithm is proposed in this paper. The proposed K-MTSCF algorithm can not only reduce the effect of data association uncertainty stemming from the ambiguity of measurements and computation complexity of data association algorithm by K-best JPDA, but also increase tracking accuracy and stability using MTSCF algorithm. The experimental results demonstrate that the proposed approach performs favorably against the state-of-the-art methods in terms of accuracy and stability for tracking multiple targets in camera networks.

Deep learning technology has grown significantly in new application scenarios such as smart cities and driverless vehicles, but its deployment needs to consume a lot of resources. It is usually difficult to execute inference task solely on resource-constrained Intelligent Internet-of-Things (IoT) devices to meet strictly service delay requirements. CNN-based inference task is usually offloaded to the edge server or cloud. However, it may lead to unstable performance and privacy leaks. To address the above challenges, this article aims to design a low latency distributed inference framework, EdgeCI, which assigns inference tasks to locally idle, connected, and resource-constrained IoT device cluster networks. EdgeCI exploits two key optimization knobs, including: (1) Auction-based Workload Assignment Scheme (AWAS), which achieves the workload balance by assigning each workload partition to the more matching IoT device; (2) Fused-Layer parallelization strategy based on non-recursive Dynamic Programming (DPFL), which is aimed at further minimizing the inference time. We have implemented EdgeCI based on PyTorch and evaluated its performance with VGG-16 and ResNet-34 image recognition models. The experimental results prove that our proposed AWAS and DPFL outperform the typical state-of-the-art solutions. When they are well combined, EdgeCI can improve inference speed by 34.72% to 43.52%. EdgeCI outperforms the state-of-the art approaches on our edge cluster.