Efficient Object Localization with Variation-Normalized Gaussianized Vectors

2011 ◽  
pp. 93-109
Author(s):  
Xiaodan Zhuang ◽  
Xi Zhou ◽  
Mark A. Hasegawa-Johnson ◽  
Thomas S. Huang
Keyword(s):  
Object Localization

3D Object Recognition and Relative Localization using a 3D sensor Embedded on a Mobile Robot

2020 ◽  
Author(s):  
Gopi Krishna Erabati
Keyword(s):  
Pose Estimation ◽  
Autonomous Systems ◽  
Microsoft Kinect ◽  
Object Localization ◽  
Initial Alignment ◽  
Relative Localization ◽  
3D Data ◽  
Industry Automation ◽  
Ofthe Object ◽  
3D Descriptors

The technology in current research scenario is marching towards automation forhigher productivity with accurate and precise product development. Vision andRobotics are domains which work to create autonomous systems and are the keytechnology in quest for mass productivity. The automation in an industry canbe achieved by detecting interactive objects and estimating the pose to manipulatethem. Therefore the object localization ( i.e., pose) includes position andorientation of object, has profound ?significance. The application of object poseestimation varies from industry automation to entertainment industry and fromhealth care to surveillance. The objective of pose estimation of objects is verysigni?cant in many cases, like in order for the robots to manipulate the objects,for accurate rendering of Augmented Reality (AR) among others.This thesis tries to solve the issue of object pose estimation using 3D dataof scene acquired from 3D sensors (e.g. Kinect, Orbec Astra Pro among others).The 3D data has an advantage of independence from object texture and invarianceto illumination. The proposal is divided into two phases : An o?ine phasewhere the 3D model template of the object ( for estimation of pose) is built usingIterative Closest Point (ICP) algorithm. And an online phase where the pose ofthe object is estimated by aligning the scene to the model using ICP, providedwith an initial alignment using 3D descriptors (like Fast Point Feature Transform(FPFH)).The approach we develop is to be integrated on two di?erent platforms :1)Humanoid robot `Pyrene' which has Orbec Astra Pro 3D sensor for data acquisition,and 2)Unmanned Aerial Vehicle (UAV) which has Intel Realsense Euclidon it. The datasets of objects (like electric drill, brick, a small cylinder, cake box)are acquired using Microsoft Kinect, Orbec Astra Pro and Intel RealSense Euclidsensors to test the performance of this technique. The objects which are used totest this approach are the ones which are used by robot. This technique is testedin two scenarios, fi?rstly, when the object is on the table and secondly when theobject is held in hand by a person. The range of objects from the sensor is 0.6to 1.6m. This technique could handle occlusions of the object by hand (when wehold the object), as ICP can work even if partial object is visible in the scene.

Superquadrics Model-based 3D Object Localization Algorithm

ROBOT ◽  
2013 ◽  
Vol 35 (4) ◽  
pp. 439 ◽  
Author(s):  
Lin WANG ◽  
Jianfu CAO ◽  
Chongzhao HAN
Keyword(s):  
Object Localization ◽  
Localization Algorithm ◽  
3D Object ◽  
Model Based

scholarly journals A Lightweight Motional Object Behavior Prediction System Harnessing Deep Learning Technology for Embedded ADAS Applications

Electronics ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 692
Author(s):  
Wen-Chia Tsai ◽  
Jhih-Sheng Lai ◽  
Kuan-Chou Chen ◽  
Vinay M.Shivanna ◽  
Jiun-In Guo
Keyword(s):  
Embedded System ◽  
Moving Objects ◽  
Object Localization ◽  
Crossing Over ◽  
Learning Technology ◽  
Prediction System ◽  
Behavior Prediction ◽  
Detection Model ◽  
Pedestrian Crossing ◽  
Object Behavior

This paper proposes a lightweight moving object prediction system to detect and recognize pedestrian crossings, vehicles cutting-in, and vehicles ahead applying emergency brakes based on a 3D Convolution network for behavior prediction. The proposed design significantly improves the performance of the conventional 3D convolution network (C3D) adapted to predict the behaviors employing behavior recognition network capable of performing object localization, which is pivotal in detecting the numerous moving objects’ behaviors, combining and verifying the detected objects with the results of the YOLO v3 detection model with that of the proposed C3D model. Since the proposed system is a lightweight CNN model requiring far lesser parameters, it can be efficiently realized on an embedded system for real-time applications. The proposed lightweight C3D model achieves 10 frames per second (FPS) on a NVIDIA Jetson AGX Xavier and yields over 92.8% accuracy in recognizing pedestrian crossing, over 94.3% accuracy in detecting vehicle cutting-in behavior, and over 95% accuracy for vehicles applying emergency brakes.

Object localization in the presence of a strong heterogeneous background in fluorescent tomography

2008 ◽  
Vol 25 (6) ◽  
pp. 1467 ◽  
Author(s):  
Pouyan Mohajerani ◽  
Ali A. Eftekhar ◽  
Ali Adibi
Keyword(s):  
Object Localization

Acquiring the ability of object localization by vision and audition through motion

2002 ◽  
Vol 85 (4) ◽  
pp. 58-67
Author(s):  
Yasuyuki Asai ◽  
Hiromichi Nakashima ◽  
Tsuyoshi Yamamura ◽  
Jie Huang ◽  
Noboru Ohnishi
Keyword(s):  
Object Localization
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