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Evolving boxes for fast vehicle detection

Li Wang, Yao Lu, Hong Wang, Yingbin Zheng, Hao Ye, Xiangyang Xue
2017 2017 IEEE International Conference on Multimedia and Expo (ICME)  
We perform fast vehicle detection from traffic surveillance cameras.  ...  for these candidate boxes.  ...  Given such evolving detection structure, we are able to accelerate the overall deep learning networks for vehicle detection.  ... 
doi:10.1109/icme.2017.8019461 dblp:conf/icmcs/WangLWZYX17 fatcat:org3clps7bfkrnk7mqzmf2qn2u

CMNet: A Connect-and-Merge Convolutional Neural Network for Fast Vehicle Detection in Urban Traffic Surveillance

Fukai Zhang, Feng Yang, Ce Li, Guan Yuan
2019 IEEE Access  
In this paper, we present a connect-and-merge convolutional neural network (CMNet) for fast detecting vehicles in complex scenes.  ...  INDEX TERMS Vehicle detection, deep fusion, residual network, convolutional neural network, real-time.  ...  Evolving Boxes adopts a proposal network and a fine-turning network for fast and accurate vehicle detection, which obtains an obvious improvement in mAP than Faster R-CNN.  ... 
doi:10.1109/access.2019.2919103 fatcat:2z65dvtv7ncefos7qjzsv5nm64

Simulation and Detection of Small Drones/Suspicious UAVs in Drone Grid

Arpit Gupta
2021 International Journal for Research in Applied Science and Engineering Technology  
This project presents a solution for autonomous real-time visual detection and tracking of hostile drones by moving cameras equipped on surveillance drones.  ...  Today's technology is evolving towards autonomous systems and the demand in autonomous drones, cars, robots, etc. has increased drastically in the past years.  ...  With the emergence of convolutional neural networks (CNNs), object detection schemes have evolved significantly in the past few years.  ... 
doi:10.22214/ijraset.2021.36144 fatcat:o3m4lcngpvbqjamkabq7fb5sa4

Scale Optimization for Full-Image-CNN Vehicle Detection [article]

Yang Gao, Shouyan Guo, Kaimin Huang, Jiaxin Chen, Qian Gong, Yang Zou, Tong Bai, Gary Overett
2018 arXiv   pre-print
In particular, we show that better alignment of the detector scale sensitivity to the extant distribution improves vehicle detection performance.  ...  We significantly increase detection AP for the KITTI dataset car class from 76.3% on our baseline Faster R-CNN detector to 83.6% in our improved detector.  ...  Evolved from R-CNN and Fast R-CNN, Ren proposed a Faster R-CNN [6] approach consisting of the Fast R-CNN method and a Region Proposals Network (RPN) sharing the same CNN features.  ... 
arXiv:1802.06926v1 fatcat:ejwgnvygbjhaxg5istt6kj4vzi

Semantic Depth Map Fusion for Moving Vehicle Detection in Aerial Video

Mahdieh Poostchi, Hadi Aliakbarpour, Raphael Viguier, Filiz Bunyak, Kannappan Palaniappan, Guna Seetharaman
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
We propose an automatic moving vehicle detection system for wide area aerial video based on semantic fusion of motion information with projected building footprint information to significantly reduce the  ...  from a network of ground-based sensors and instrumented vehicles.  ...  Ground-truth for the 200 frames of the ABQ dataset was provided by Arslan Basharat at Kitware.  ... 
doi:10.1109/cvprw.2016.196 dblp:conf/cvpr/PoostchiAVBPS16 fatcat:ze66xd5yqbe6nbcqyqjt7suwlm

A method based on Multi-Convolution layers Joint and Generative Adversarial Networks for Vehicle Detection

2019 KSII Transactions on Internet and Information Systems  
Finally, these candidate vehicle detection boxes are optimized to obtain the final vehicle detection boxes by the Fine-Tuning Network(FTN).  ...  In order to achieve rapid and accurate detection of vehicle objects in complex traffic conditions, we propose a novel vehicle detection method.  ...  Evolved Region Proposal Network In real road traffic,the background objects of the vehicle are often diverse. Thus, filtering out bad candidate boxes is crucial for the vehicle detection algor ithm.  ... 
doi:10.3837/tiis.2019.04.003 fatcat:eg4vslw7mbeyrjnhhd3niklxxe

Online learning neural tracker

S. Suresh, F. Brémond, M. Thonnat, H.J. Kim
2011 Neurocomputing  
The signature of the posterior probability map is used to adapt the bounding box to handle the scale change and improper initialization.  ...  In the proposed tracking system, we propose a new mobile object detection module which identifies new mobile objects in the scene and then OLNT faithfully locates them in subsequent frames.  ...  The bounding-box initialization using mobile object detection is used for fixed camera sequences and hand initialization is used for moving camera sequences. A.  ... 
doi:10.1016/j.neucom.2011.02.013 fatcat:da5kgxbx6na6jhvx2h6mnypoam

Vehicle Path Planning with Multicloud Computation Services

Po-Tong Wang, Shao-Yu Lin, Jia-Shing Sheu
2021 Advances in Technology Innovation  
The Euclidean distance and the inequality based on the detected marker box data are used for vehicle path planning.  ...  The proposed method integrates the bounding box information provided by multiple cloud object detection services to detect navigable areas and plan routes.  ...  Literature Review and Methodology Object detection is a technique for classifying objects in an image, and object detection technology has evolved considerably. LeCun et al.  ... 
doi:10.46604/aiti.2021.7192 fatcat:62wimeoum5hqlfwz3r6pruqbhq

A comparison of deep learning algorithms on image data for detecting floodwater on roadways

Salih Sarp, Murat Kuzlu, Yanxiao Zhao, Mecit Cetin, Ozgur Guler
2021 Computer Science and Information Systems  
Detection and segmentation of (partially) flooded roadways are essential inputs for vehicle routing and traffic management systems.  ...  Object detection and segmentation algorithms evolved significantly in the last decade.  ...  The Fast-R-CNN method, Fast Region-based Convolutional Neural Network, is an improved version of R-CNN, which requires a long time for object detection.  ... 
doi:10.2298/csis210313058s fatcat:7uuvum23vnhpfpwnqfgixqpjgy

Sensor Fusion of Camera and Cloud Digital Twin Information for Intelligent Vehicles [article]

Yongkang Liu, Ziran Wang, Kyungtae Han, Zhenyu Shou, Prashant Tiwari, John H. L. Hansen
2020 arXiv   pre-print
Target vehicle bounding box is drawn and matched by combining results of object detector running on ego vehicle and position information from the cloud.  ...  Visual guidance for drivers is essential under this situation to prevent potential risks.  ...  Output: Distance set ( ) from detected vehicles to camera. 1: for each detected bounding box ∈ 2: decrease the box size according to the box resize threshold ( ℎ), select the lower ¼ area of the box as  ... 
arXiv:2007.04350v1 fatcat:mhhivcufgjh7zocqok76riuibm

Fast Object Detection Using Multistage Particle Window Deformable Part Model

Wei-Ta Chu, Ming-Hung Hsu
2014 2014 IEEE International Symposium on Multimedia  
objects in images, and is able to efficiently detect vehicles and pedestrians in on-road videos.  ...  For object detection, evaluating all sliding windows at various scales draws a computational efficiency issue.  ...  On-Road Pedestrian Detection and Vehicle Detection Datasets. Table I and Table II respectively show detailed information of the datasets used for on-road pedestrian detection and vehicle detection.  ... 
doi:10.1109/ism.2014.23 dblp:conf/ism/ChuH14 fatcat:ezy42ootyjfndevfnevbf7773q

Efficient Techniques for Dynamic Vehicle Detection [chapter]

Anna Petrovskaya, Sebastian Thrun
2009 Springer Tracts in Advanced Robotics  
Fast detection of moving vehicles is crucial for safe autonomous urban driving.  ...  We present the vehicle detection algorithm developed for our entry in the Urban Grand Challenge, an autonomous driving race organized by the U.S. Government in 2007.  ...  The authors thank all team members for their hard work. The Stanford Racing Team is indebted to DARPA for creating the UGC, and for its financial support under the Track A Program.  ... 
doi:10.1007/978-3-642-00196-3_10 fatcat:i4vxkwq5qfeatbax2ruvp6i3da

Automating Defense Against Adversarial Attacks: Discovery of Vulnerabilities and Application of Multi-INT Imagery to Protect Deployed Models [article]

Josh Kalin, David Noever, Matthew Ciolino, Dominick Hambrick, Gerry Dozier
2021 arXiv   pre-print
For machine learning, we demonstrate these methods with 3-color channels plus infrared for vehicles.  ...  When applying popular model architectures like MobileNetV2, known vulnerabilities expose the model to counter-attacks, either mislabeling a known class or altering box location.  ...  Figure 1 . 1 Vehicle Detection in Aerial Imagery (VEDAI) sample data showing different vehicles in different orientations from an overhead visible sensor.  ... 
arXiv:2103.15897v1 fatcat:qxatkqk3ufbfvg24ampvcvjclm

Improved vehicle detection systems with double-layer LSTM modules

Wei-Jong Yang, Wan-Ju Liow, Shao-Fu Chen, Jar-Ferr Yang, Pau-Choo Chung, Songan Mao
2022 EURASIP Journal on Advances in Signal Processing  
performance of the vehicles in various conditions, including the newly-appeared, the detected and the gradually-disappearing vehicles.  ...  With stage-by-stage evaluations, the experimental results show that the proposed vehicle detection system with dLSTM modules can precisely detect the vehicles without increasing computations.  ...  It is noted that all the false positive cases are the fast-disappeared vehicles, which are with fast speed.  ... 
doi:10.1186/s13634-022-00839-6 doaj:2d60bce8f1ff4fe6a1a16386c8304dda fatcat:rhu67fc44raublcyrorqaqtw6i

Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation

Fukai Zhang, Ce Li, Feng Yang
2019 Sensors  
Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance.  ...  Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time.  ...  Furthermore, DP-SSD300 can prevent detecting multiple boxes for one vehicle and improve the detection rate for small vehicles as shown in Figure 1d .  ... 
doi:10.3390/s19030594 fatcat:xmzvhdrwrrdjdak57ebzbhoo6u
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