Compute and memory demands of state-of-the-art deep learning methods are still a shortcoming that must be addressed to make them useful at IoT end-nodes. In particular, recent results depict a hopeful prospect for image processing using Convolutional Neural Netwoks, CNNs, but the gap between software and hardware implementations is already considerable for IoT and mobile edge computing applications due to their high power consumption. This proposal performs low-power and real time deep learning-based multiple object visual tracking implemented on an NVIDIA Jetson TX2 development kit. It includes a camera and wireless connection capability and it is battery powered for mobile and outdoor applications. A collection of representative sequences captured with the onboard camera, dETRUSC video dataset, is used to exemplify the performance of the proposed algorithm and to facilitate benchmarking. The results in terms of power consumption and frame rate demonstrate the feasibility of deep learning algorithms on embedded platforms although more effort to joint algorithm and hardware design of CNNs is needed.

Computer vision systems for traffic monitoring represent an essential tool for a broad range of traffic surveillance applications. Two of the most noteworthy challenges for these systems are the real-time operation with hundreds of vehicles and the total occlusions which hinder the tracking of the vehicles. In this paper, we present a traffic monitoring approach that deals with these two challenges based on three modules: detection, tracking and data association.First, vehicles are identified through a deep learning based detector. Second, tracking is performed with a combination of a Discriminative Correlation Filter and a Kalman Filter. This permits to estimate the tracking error in order to make tracking more robust and reliable. Finally, the data association through the Hungarian algorithm combines the information of the previous steps. The contributions are: (i) a real-time traffic monitoring system robust to occlusions that can process more than four hundred vehicles simultaneously; and (ii) the application of the system to anomaly detection in traffic and roundabout input/output analysis. The system has been evaluated with more than two thousand vehicles in real-life videos.

Real-time visual object tracking provides every object of interest with a unique identity and a trajectory across video frames. This is a fundamental task of many video analytics applications like traffic monitoring, or video surveillance in general. The development of real-time multiple object tracking systems on low-power edge devices as IoT nodes, without compromising accuracy, is a challenge due to the limited computing capacity of said devices. This might rule out the best in-class computer vision solutions, which nowadays are based on deep learning, and thus, they are very hardware demanding. This paper meets this challenge with a multiple object detection and tracking system that employs cutting-edge deep learning architectures on an embedded GPU while operating in real-time. For this purpose, a system has been designed that extends a joint architecture of tracking and detection by adding a module comprised of appearance-based and movement-based trackers that allow to maintain the identity of the objects of interest for longer periods of time while alleviating the burden of the detector. Our system is mapped onto an embedded GPU platform, cutting down power consumption significantly with respect to a server GPU. Tracking performance metrics show a 51.1% in Multiple Object Tracking Accuracy (MOTA) on the MOT16 dataset. This, in conjunction with a real-time processing speed of 25.2 FPS for up to 45 simultaneous objects and low power consumption of 15W, make our system an ideal solution for a wide-range of video analytics applications.Index Terms-edge computing, deep learning, multiple object tracking I. INTRODUCTIONF ROM the point of view of the type of information to be transferred to the end user, computer vision applications can be divided into two large groups: (i) those that send a continuous video stream, and (ii) those that only need to send data calculated from video analysis. The latter are especially suitable for edge computing. This is the case of video analytics scenarios, where the computer vision system makes most or all the computing on the edge, before sending the resultwhich usually does not include video, but data-to the end user [1]- [3]. Some examples of video analytics are: highway tolls, where only vehicle counting and classification in light This research was partially funded by the Spanish Ministry of Science, Innovation and Universities under grants TIN2017-84796-C2-1-R and RTI2018-097088-B-C32, and the Galician Ministry of Education, Culture and Universities under grants ED431C 2018/29, ED431C 2017/69 and accreditation 2016-2019, ED431G/08. These grants are co-funded by the European Regional Development Fund (ERDF/FEDER program).