scholarly journals Convolutional Neural Network-Based Real-Time Object Detection and Tracking for Parrot AR Drone 2

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 69575-69584 ◽  
Author(s):  
Ali Rohan ◽  
Mohammed Rabah ◽  
Sung-Ho Kim
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Object Detection And Tracking ◽  
Detection And Tracking ◽  
Ar Drone

Real-Time 3D object detection using improved convolutional neural network based on image-driven point cloud

2021 ◽  
Vol 14 ◽  
Author(s):  
Zhiyong Gao ◽  
Jianhong Xiang
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Point Cloud ◽  
Point Clouds ◽  
3D Point Cloud ◽  
3D Object ◽  
3D Object Detection ◽  
Instance Segmentation

Background: While detecting the object directly from the 3D point cloud, the natural 3D patterns and invariance of 3D data are often obscure. Objective: In this work, we aimed at studying the 3D object detection from discrete, disordered and sparse 3D point clouds. Methods: The CNN is composed of the frustum sequence module, 3D instance segmentation module S-NET, 3D point cloud transformation module T-NET, and 3D boundary box estimation module E-NET. The search space of the object is determined by the frustum sequence module. The instance segmentation of the point cloud is performed by the 3D instance segmentation module. The 3D coordinates of the object are confirmed by the transformation module and the 3D bounding box estimation module. Results: Evaluated on KITTI benchmark dataset, our method outperforms the state of the art by remarkable margins while having real-time capability. Conclusion: We achieve real-time 3D object detection by proposing an improved convolutional neural network (CNN) based on image-driven point clouds.

scholarly journals Design of a Scalable and Fast YOLO for Edge-Computing Devices

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6779
Author(s):  
Byung-Gil Han ◽  
Joon-Goo Lee ◽  
Kil-Taek Lim ◽  
Doo-Hyun Choi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Processing Speed ◽  
Edge Computing ◽  
Light Weight ◽  
Detection Technology ◽  
Accuracy Performance ◽  
Speed And Accuracy

With the increase in research cases of the application of a convolutional neural network (CNN)-based object detection technology, studies on the light-weight CNN models that can be performed in real time on the edge-computing devices are also increasing. This paper proposed scalable convolutional blocks that can be easily designed CNN networks of You Only Look Once (YOLO) detector which have the balanced processing speed and accuracy of the target edge-computing devices considering different performances by exchanging the proposed blocks simply. The maximum number of kernels of the convolutional layer was determined through simple but intuitive speed comparison tests for three edge-computing devices to be considered. The scalable convolutional blocks were designed in consideration of the limited maximum number of kernels to detect objects in real time on these edge-computing devices. Three scalable and fast YOLO detectors (SF-YOLO) which designed using the proposed scalable convolutional blocks compared the processing speed and accuracy with several conventional light-weight YOLO detectors on the edge-computing devices. When compared with YOLOv3-tiny, SF-YOLO was seen to be 2 times faster than the previous processing speed but with the same accuracy as YOLOv3-tiny, and also, a 48% improved processing speed than the YOLOv3-tiny-PRN which is the processing speed improvement model. Also, even in the large SF-YOLO model that focuses on the accuracy performance, it achieved a 10% faster processing speed with better accuracy of 40.4% [email protected] in the MS COCO dataset than YOLOv4-tiny model.

Real-time object detection and tracking in omni-directional surveillance using GPU

2015 ◽  
Author(s):  
Florian Depraz ◽  
Vladan Popovic ◽  
Beat Ott ◽  
Peter Wellig ◽  
Yusuf Leblebici
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Object Detection And Tracking ◽  
Detection And Tracking

scholarly journals SCNN: A General Distribution Based Statistical Convolutional Neural Network with Application to Video Object Detection

2019 ◽  
Vol 33 ◽  
pp. 5321-5328 ◽  
Author(s):  
Tianchen Wang ◽  
Jinjun Xiong ◽  
Xiaowei Xu ◽  
Yiyu Shi
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Correlated Data ◽  
General Distribution ◽  
Video Object ◽  
Human Beings ◽  
Detection And Tracking ◽  
General Network ◽  
Multiple Frames

Various convolutional neural networks (CNNs) were developed recently that achieved accuracy comparable with that of human beings in computer vision tasks such as image recognition, object detection and tracking, etc. Most of these networks, however, process one single frame of image at a time, and may not fully utilize the temporal and contextual correlation typically present in multiple channels of the same image or adjacent frames from a video, thus limiting the achievable throughput. This limitation stems from the fact that existing CNNs operate on deterministic numbers. In this paper, we propose a novel statistical convolutional neural network (SCNN), which extends existing CNN architectures but operates directly on correlated distributions rather than deterministic numbers. By introducing a parameterized canonical model to model correlated data and defining corresponding operations as required for CNN training and inference, we show that SCNN can process multiple frames of correlated images effectively, hence achieving significant speedup over existing CNN models. We use a CNN based video object detection as an example to illustrate the usefulness of the proposed SCNN as a general network model. Experimental results show that even a nonoptimized implementation of SCNN can still achieve 178% speedup over existing CNNs with slight accuracy degradation.

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