scholarly journals ATG-PVD: Ticketing Parking Violations on A Drone

2020 ◽  
Author(s):  
Hengli Wang ◽  
Yuxuan Liu ◽  
Huaiyang Huang ◽  
Yuheng Pan ◽  
Wenbin Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Experimental Results ◽  
Visual Slam ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Car Detection ◽  
Speed And Accuracy

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.

scholarly journals ATG-PVD: Ticketing Parking Violations on A Drone

2020 ◽  
Author(s):  
Hengli Wang ◽  
Yuxuan Liu ◽  
Huaiyang Huang ◽  
Yuheng Pan ◽  
Wenbin Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Optical Flow ◽  
State Of The Art ◽  
Experimental Results ◽  
Visual Slam ◽  
Flow Estimation ◽  
Optical Flow Estimation ◽  
Car Detection ◽  
Speed And Accuracy

In this paper, we introduce a novel suspect-and-investigate framework, which can be easily embedded in a drone for automated parking violation detection (PVD). Our proposed framework consists of: 1) SwiftFlow, an efficient and accurate convolutional neural network (CNN) for unsupervised optical flow estimation; 2) Flow-RCNN, a flow-guided CNN for car detection and classification; and 3) an illegally parked car (IPC) candidate investigation module developed based on visual SLAM. The proposed framework was successfully embedded in a drone from ATG Robotics. The experimental results demonstrate that, firstly, our proposed SwiftFlow outperforms all other state-of-the-art unsupervised optical flow estimation approaches in terms of both speed and accuracy; secondly, IPC candidates can be effectively and efficiently detected by our proposed Flow-RCNN, with a better performance than our baseline network, Faster-RCNN; finally, the actual IPCs can be successfully verified by our investigation module after drone re-localization.

scholarly journals Temporal Motionless Analysis of Video using CNN in MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Amit Singh ◽  
Dilip Kumar Prasad ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Training Image ◽  
Experimental Results ◽  
Current Frame ◽  
Previous Image

<div><div><div><p>This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.</p></div></div></div>

scholarly journals Global and Local Attention-Based Free-Form Image Inpainting

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3204
Author(s):  
S. M. Nadim Uddin ◽  
Yong Ju Jung
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Image Inpainting ◽  
Experimental Results ◽  
Free Form ◽  
Local Similarity ◽  
Global And Local ◽  
Similarity Information

Deep-learning-based image inpainting methods have shown significant promise in both rectangular and irregular holes. However, the inpainting of irregular holes presents numerous challenges owing to uncertainties in their shapes and locations. When depending solely on convolutional neural network (CNN) or adversarial supervision, plausible inpainting results cannot be guaranteed because irregular holes need attention-based guidance for retrieving information for content generation. In this paper, we propose two new attention mechanisms, namely a mask pruning-based global attention module and a global and local attention module to obtain global dependency information and the local similarity information among the features for refined results. The proposed method is evaluated using state-of-the-art methods, and the experimental results show that our method outperforms the existing methods in both quantitative and qualitative measures.

scholarly journals Temporal Motionless Analysis of Video using CNN in MPSoC

2020 ◽  
Author(s):  
Somdip Dey ◽  
Amit Singh ◽  
Dilip Kumar Prasad ◽  
Klaus D. Mcdonald-Maier
Keyword(s):  
Neural Network ◽  
Energy Consumption ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Training Image ◽  
Experimental Results ◽  
Current Frame ◽  
Previous Image

<div><div><div><p>This paper proposes a novel human-inspired methodology called IRON-MAN (Integrated RatiONal prediction and Motionless ANalysis of videos) on mobile multi-processor systems-on-chips (MPSoCs). The methodology integrates analysis of the previous image frames of the video to represent the analysis of the current frame in order to perform Temporal Motionless Analysis of the Video (TMAV). This is the first work on TMAV using Convolutional Neural Network (CNN) for scene prediction in MPSoCs. Experimental results show that our methodology outperforms state-of-the-art. We also introduce a metric named, Energy Consumption per Training Image (ECTI) to assess the suitability of using a CNN model in mobile MPSoCs with a focus on energy consumption of the device.</p></div></div></div>

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