Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Blanco-Filgueira, B.; Garcia-Lesta, D.; Fernández-Sanjurjo, Mauro; Brea, V.M.; López, P.

doi:10.1109/jiot.2019.2902141

Cited by 92 publications

(41 citation statements)

References 40 publications

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“…This has been primarily possible due to the excellent ability of deep learning in discovering intricate structures in high-dimensional data. Examples include character recognition [16], gesture recognition [17], speech recognition (e.g., in Google Now, Siri, or click-through prediction on an advertisement) [18]- [20], document processing [21]- [23], natural language processing [24], [25], video classification [26], image classification [27]- [32], face detection and recognition [33], [34], robot navigation [35]- [37], realtime multiple object tracking [38], financial forecasting [39], and medical diagnosis systems [40]- [42], to name a few.…”

Section: A Applications Of Deep Learning Networkmentioning

confidence: 99%

FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

2019

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Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged, and has demonstrated its ability and effectiveness in solving complex learning problems not possible before. In particular, convolution neural networks (CNNs) have demonstrated their effectiveness in image detection and recognition applications. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve desired performance levels. Consequently, hardware accelerators that use application specific integrated circuits (ASICs), field programmable gate arrays (FPGAs), and graphic processing units (GPUs) have been employed to improve the throughput of CNNs. More precisely, FPGAs have been recently adopted for accelerating the implementation of deep learning networks due to their ability to maximize parallelism as well as due to their energy efficiency. In this paper, we review recent existing techniques for accelerating deep learning networks on FPGAs. We highlight the key features employed by the various techniques for improving the acceleration performance. In addition, we provide recommendations for enhancing the utilization of FPGAs for CNNs acceleration. The techniques investigated in this paper represent the recent trends in FPGA-based accelerators of deep learning networks. Thus, this review is expected to direct the future advances on efficient hardware accelerators and to be useful for deep learning researchers.

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Section: A Applications Of Deep Learning Networkmentioning

confidence: 99%

FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

2019

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“…The results highlighted the effectiveness of the algorithm under real challenging scenarios in different environmental conditions, such as low light and high contrast in the tracking phase and not consider in the detection phase. Hossain, S et al [21] proposed an application based on a DL technique, implemented on a computer system integrated with a drone, to track the objects in real-time. Experiments with the proposed algorithms demonstrated good efficacy using a multi-rotor aircraft.…”

Section: Related Workmentioning

confidence: 99%

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Abdulghafoor¹,

Abdullah²

2020

IJIES

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Multi-object detection and tracking systems represent one of the basic and important tasks of surveillance and video traffic systems. Recently. The proposed tracking algorithms focused on the detection mechanism. It showed significant improvement in performance in the field of computer vision. Though. It faced many challenges and problems, such as many blockages and segmentation of paths, in addition to the increasing number of identification keys and false-positive paths. In this work, an algorithm was proposed that integrates information on appearance and visibility features to improve the tracker's performance. It enables us to track multiple objects throughout the video and for a longer period of clogging and buffer a number of ID switches. An effective and accurate data set, tools, and metrics were also used to measure the efficiency of the proposed algorithm. The experimental results show the great improvement in the performance of the tracker, with high accuracy of more than 65%, which achieves competitive performance with the existing algorithms.

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“…Tijtgat et al [1] compare YOLOV2 and tiny YOLOV2 among a few other classic object detection techniques both in terms of speed and accuracy on a Jetson TX2 platform for a UAV warning system. The same hardware is used by Blanco-Filgueira et al [3]. They propose an embedded real-time multi-object tracker based on a foregroundbackground detector and the GOTURN tracker.…”

Section: Related Workmentioning

confidence: 99%

“…The target platforms are NVIDIA Jetson TX2 and NVIDIA Xavier. While previous works run embedded object detectors/trackers on NVIDIA Jetson platforms [1,2,3], and stick to simple implementations, we go a number of steps further in optimizing our network for the target platform. We fuse layers and quantize calculations from floats to half floats (TX2) and even to 8-bit integers (Xavier).…”

Section: Introductionmentioning

confidence: 99%

Super accurate low latency object detection on a surveillance UAV

Vandersteegen

Beeck

Goedemé

2019

2019 16th International Conference on Machine Vision Applications (MVA)

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Drones have proven to be useful in many industry segments such as security and surveillance, where e.g. on-board real-time object tracking is a necessity for autonomous flying guards. Tracking and following suspicious objects is therefore required in real-time on limited hardware. With an object detector in the loop, low latency becomes extremely important. In this paper, we propose a solution to make object detection for UAVs both fast and super accurate. We propose a multi-dataset learning strategy yielding top eye-sky object detection accuracy. Our model generalizes well on unseen data and can cope with different flying heights, optically zoomed-in shots and different viewing angles. We apply optimization steps such that we achieve minimal latency on embedded on-board hardware by fusing layers, quantizing calculations to 16-bit floats and 8bit integers, with negligible loss in accuracy. We validate on NVIDIA's Jetson TX2 and Jetson Xavier platforms where we achieve a speed-wise performance boost of more than 10×.

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Deep Learning-Based Multiple Object Visual Tracking on Embedded System for IoT and Mobile Edge Computing Applications

Cited by 92 publications

References 40 publications

FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review

Enhancement Performance of Multiple Objects Detection and Tracking for Real-time and Online Applications

Super accurate low latency object detection on a surveillance UAV

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