- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11259 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part IV) || Asymmetric Two-Stream Networks for RGB-Disparity Based Object Detection
摘要： Currently, most methods of object detection are monocular-based. However, due to the sensitivity to color, these methods can not handle many hard samples. With the depth information, disparity maps are helpful to get over this problem. In this paper, we propose the asymmetric two-stream networks for RGB-Disparity based object detection. Our method consists of two networks, Disparity Representations Mining Network (DRMN) and Muti-Modal Detection Network (MMDN), to combine RGB and disparity data for more accurate detection. Unlike normal two-stream networks, our model is asymmetric because of the di?erent capacity of RGB and disparity data. We are the ?rst to propose a deep learning based framework utilizing only binocular information for object detection. The experiment results on KITTI and our proposed BPD dataset demonstrate that our method can achieve a signi?cant increase in performance e?ciently and get the state-of-the-art.
关键词： Two-stream networks,Object detection,RGBD data
更新于2025-09-23 15:21:21
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Real-Time Texture-less Object Recognition on Mobile Devices
摘要： This paper presents a technique for real-time texture-less object recognition and tracking on mobile devices. Our proposed algorithm is an even lighter-weight version of the recent state-of-the-art binary-based texture-less object detector BIND (Binary Integrated Net Descriptor), primarily customized for mobile device applications. This modification, termed BIND-Lite, employs various techniques to overcome the low-computational power of current mobile devices, while mostly retaining the texture-less object detection robustness of the original BIND. On current generation mobile devices, BIND-Lite was able to achieve runtime rates of up to 30 frames per second. To evaluate our algorithm, we have also designed a mobile augmented reality application coined IMPRINT, which renders logos/images onto detected objects to showcase BIND-Lite in a real-time mobile augmented reality setting.
关键词： mobile augmented reality,Real Time,object detection,binary descriptor,texture-less,binary matching
更新于2025-09-23 15:21:01
-
Real-time Concealed Object Detection from Passive Millimeter Wave Images Based on the YOLOv3 Algorithm
摘要： The detection of objects concealed under people’s clothing is a very challenging task, which has crucial applications for security. When testing the human body for metal contraband, the concealed targets are usually small in size and are required to be detected within a few seconds. Focusing on weapon detection, this paper proposes using a real-time detection method for detecting concealed metallic weapons on the human body applied to passive millimeter wave (PMMW) imagery based on the You Only Look Once (YOLO) algorithm, YOLOv3, and a small sample dataset. The experimental results from YOLOv3-13, YOLOv3-53, and Single Shot MultiBox Detector (SSD) algorithm, SSD-VGG16, are compared ultimately, using the same PMMW dataset. For the perspective of detection accuracy, detection speed, and computation resource, it shows that the YOLOv3-53 model had a detection speed of 36 frames per second (FPS) and a mean average precision (mAP) of 95% on a GPU-1080Ti computer, more e?ective and feasible for the real-time detection of weapon contraband on human body for PMMW images, even with small sample data.
关键词： YOLOv3,real-time,neural network,concealed object detection,deep learning,passive millimeter wave
更新于2025-09-23 15:19:57
-
[IEEE 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) - Orlando, FL, USA (2018.12.17-2018.12.20)] 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) - Fine Object Detection in Automated Solar Panel Layout Generation
摘要： A solar panel layout is a diagram of a roof, with the roof edges and obstacles marked. Currently, the user has to manually draw boundary over each obstacle in a tedious and meticulous manner. In this work, we have built a framework using the existing object detection models. We have leveraged the power of traditional edge detection algorithms, fusing with the cutting-edge machine learning based object detection frameworks. This fusion results in a framework capable of detecting objects to their exact edges. Thus, the boundary of each obstacle in a solar panel can be generated automatically with the edge pixel count variation of less than 25% compared to the ground truth.
关键词： machine learning,object detection,edge detection
更新于2025-09-19 17:15:36
-
[IEEE 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Xi'an, China (2018.11.7-2018.11.10)] 2018 Eighth International Conference on Image Processing Theory, Tools and Applications (IPTA) - Video Tracking of Insect Flight Path: Towards Behavioral Assessment
摘要： In this paper, we propose a cohort of new methods that cooperate together to improve the detection/tracking of mosquitos in a 2D video clip. A commonly recognized challenge in the biotechnology research field is evaluating the effect of a repellent which entails tracking the unpredictable flight paths of the insects, which may be swift flying or slow moving. Our work presented in this paper provides an efficient tool to deal with tracking the small insects with unpredictable moving patterns by proposing a new dual foreground and background modeling/updating system for target detecting and tracking. The proposed processing elements take advantage of the similarity of the frames and use the estimated speeds to collectively capture the relevant information and contribute in concert to ensure fast and accurate measurement to reach the goal of behavior evaluation of mosquitos in response to a repellent.
关键词： multiple target tracking,Video object detection,foreground and background modeling/updating
更新于2025-09-19 17:15:36
-
[IEEE 2018 Digital Image Computing: Techniques and Applications (DICTA) - Canberra, Australia (2018.12.10-2018.12.13)] 2018 Digital Image Computing: Techniques and Applications (DICTA) - 3D Multiview Basketball Players Detection and Localization Based on Probabilistic Occupancy
摘要： This paper addresses the issue of 3D multiview basketball players detection and localization. Existing methods for this problem typically take background subtraction as input, which limits the accuracy of localization and the performance of further object tracking. Moreover, the performance of background subtraction based methods is heavily impacted by the occlusions in crowded scenes. In this paper, we propose an innovative method which jointly implements deep learning based player detection and occupancy probability based player localization. What’s more, a new Bayesian model of the localization algorithms is developed, which uses foreground information from fisheye cameras to setup meaningful initialization values in the first step of iteration, in order to not only eliminate ambiguous detection, but also accelerate computational processes. Experimental results on real basketball game data demonstrate that our methods significantly improve the performance compared with current methods, by eliminating missed and false detection, as well as increasing probabilities of positive results.
关键词： Pedestrian Localization,Object Detection,Multiview
更新于2025-09-19 17:15:36
-
[IEEE 2019 IEEE Intelligent Transportation Systems Conference - ITSC - Auckland, New Zealand (2019.10.27-2019.10.30)] 2019 IEEE Intelligent Transportation Systems Conference (ITSC) - Training a Fast Object Detector for LiDAR Range Images Using Labeled Data from Sensors with Higher Resolution
摘要： In this paper, we describe a strategy for training neural networks for object detection in range images obtained from one type of LiDAR sensor using labeled data from a different type of LiDAR sensor. Additionally, an efficient model for object detection in range images for use in self-driving cars is presented. Currently, the highest performing algorithms for object detection from LiDAR measurements are based on neural networks. Training these networks using supervised learning requires large annotated datasets. Therefore, most research using neural networks for object detection from LiDAR point clouds is conducted on a very small number of publicly available datasets. Consequently, only a small number of sensor types are used. We use an existing annotated dataset to train a neural network that can be used with a LiDAR sensor that has a lower resolution than the one used for recording the annotated dataset. This is done by simulating data from the lower resolution LiDAR sensor based on the higher resolution dataset. Furthermore, improvements to models that use LiDAR range images for object detection are presented. The results are validated using both simulated sensor data and data from an actual lower resolution sensor mounted to a research vehicle. It is shown that the model can detect objects from 360? range images in real time.
关键词： self-driving cars,object detection,LiDAR,range images,neural networks
更新于2025-09-19 17:13:59
-
[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Optical Spectrum Measurement and Analysis for Flexible WDM System Using Faster R-CNN-based Object Detection
摘要： An optical spectrum measurement and analysis method using faster R-CNN-based object detection technique is proposed. This method can simultaneously analyze OSNR, bandwidth, and center wavelength of FWDM system. The average accuracy reaches 98.6%.
关键词： Optical performance monitoring,Object detection,Optical spectrum analysis
更新于2025-09-16 10:30:52
-
[IEEE 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Huangshan, China (2019.8.5-2019.8.8)] 2019 18th International Conference on Optical Communications and Networks (ICOCN) - Object Wedge Angle and Direction Identification Using Machine Learning Algorithms
摘要： We demonstrate identification of object wedge angle and direction using machine learning algorithms based on received beam intensity profiles. CNN outperforms other algorithms with 100% accuracy. Proposed technique reduces the complexity of hardware implementation.
关键词： Image recognition,Machine learning,Object detection,Remote sensing
更新于2025-09-16 10:30:52
-
[IEEE 2018 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - Montréal, QC, Canada (2018.6.3-2018.6.7)] 2018 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (Wow) - The Optimization of Auxiliary Detection Coil for Metal Object Detection in Wireless Power Transfer
摘要： For the safety of customers and WPT Electrical Vehicles (WPTEVs), Metal Object Detection (MOD) is a necessary module in the whole WPT system design. In this paper, auxiliary detection coil which utilizes the influence of metal objects on the detection voltage is used as the method to achieve the MOD and an equation which can optimize the design of MOD auxiliary detection coil is proposed. To optimize the detection coil, approximate quantitative description of voltage change in the detection coil is done by modeling the effect of a metal object. A comparison between the simulation and equation’s numerical analysis results is presented that verifies the use of proposed equation. The influence of the radius, turns and position of auxiliary detection coil on voltage variation are also analyzed. And at the end of the paper, the testing result is proposed to verify the feasibility of the optimization method of this auxiliary detection coil design.
关键词： metal object detection (MOD),wireless power transfer (WPT),auxiliary detection coil
更新于2025-09-11 14:15:04