Abstract
This paper presents the T-RexNet approach to detect small moving objects in videos by using a deep neural network. T-RexNet combines the advantages of Single-Shot-Detectors with a specific feature-extraction network, thus overcoming the known shortcomings of Single-Shot-Detectors in detecting small objects. The deep convolutional neural network includes two parallel paths: the first path processes both the original picture, in gray-scale format, and differences between consecutive frames; in the second path, differences between a set of three consecutive frames is only handled. As compared with generic object detectors, the method limits the depth of the convolutional network to make it less sensible to high-level features and easier to train on small objects. The simple, Hardware-efficient architecture attains its highest accuracy in the presence of videos with static framing. Deploying our architecture on the NVIDIA Jetson Nano edge-device shows its suitability to embedded systems. To prove the effectiveness and general applicability of the approach, real-world tests assessed the method performances in different scenarios, namely, aerial surveillance with the WPAFB 2009 dataset, civilian surveillance using the Chinese University of Hong Kong (CUHK) Square dataset, and fast tennis-ball tracking, involving a custom dataset. Experimental results prove that T-RexNet is a valid, general solution to detect small moving objects, which outperforms in this task generic existing object-detection approaches. The method also compares favourably with application-specific approaches in terms of the accuracy vs. speed trade-off.
Highlights
The recent growth of industrial applications for object detection stimulates the research community toward novel solutions
Thanks to GPUs, object detection solutions based on deep learning can support real time applications; the edge-computing market offers a variety of relatively inexpensive devices for Artificial-Intelligence (AI): microprocessors [8], hardware accelerators [9], up to complete Systems on Module (SoM), such as the Jetson series by NVIDIA [10], and machine vision cameras such as the JeVois A33 and Sipeed Maix Bit, used in [11]
The system benefits from the versatility of an endto-end fully convolutional neural network, it processes differences between frames to involve motion information, and relies on the efficiency of MobileNet-based convolutions to integrate visual and motion data
Summary
The recent growth of industrial applications for object detection stimulates the research community toward novel solutions. Thanks to GPUs, object detection solutions based on deep learning can support real time applications; the edge-computing market offers a variety of relatively inexpensive devices for Artificial-Intelligence (AI): microprocessors [8], hardware accelerators [9], up to complete Systems on Module (SoM), such as the Jetson series by NVIDIA [10], and machine vision cameras such as the JeVois A33 and Sipeed Maix Bit, used in [11]. These tools rely on GPUs and a collection of software optimisations to deploy computationally intensive tasks, such as AI inference, on resource-constrained hardware. The basic idea consists in working out the difference between a frame and the background model of the scene acquired by the same camera; the time-difference information highlights the changes caused by moving objects
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