scholarly journals DeepGamble: Towards unlocking real-time player intelligence using multi-layer instance segmentation and attribute detection

2020 ◽  
Author(s):  
Danish Syed ◽  
Naman Gandhi ◽  
Arushi Arora ◽  
Nilesh Kadam
Keyword(s):  
Real Time ◽  
Instance Segmentation

scholarly journals Real-Time Instance Segmentation of Traffic Videos for Embedded Devices

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 275
Author(s):  
Ruben Panero Martinez ◽  
Ionut Schiopu ◽  
Bruno Cornelis ◽  
Adrian Munteanu
Keyword(s):  
Real Time ◽  
Network Architecture ◽  
Training Procedure ◽  
Segmentation Method ◽  
Embedded Devices ◽  
Network Training ◽  
Assignment Algorithm ◽  
Ablation Study ◽  
Reduced Rate ◽  
Instance Segmentation

The paper proposes a novel instance segmentation method for traffic videos devised for deployment on real-time embedded devices. A novel neural network architecture is proposed using a multi-resolution feature extraction backbone and improved network designs for the object detection and instance segmentation branches. A novel post-processing method is introduced to ensure a reduced rate of false detection by evaluating the quality of the output masks. An improved network training procedure is proposed based on a novel label assignment algorithm. An ablation study on speed-vs.-performance trade-off further modifies the two branches and replaces the conventional ResNet-based performance-oriented backbone with a lightweight speed-oriented design. The proposed architectural variations achieve real-time performance when deployed on embedded devices. The experimental results demonstrate that the proposed instance segmentation method for traffic videos outperforms the you only look at coefficients algorithm, the state-of-the-art real-time instance segmentation method. The proposed architecture achieves qualitative results with 31.57 average precision on the COCO dataset, while its speed-oriented variations achieve speeds of up to 66.25 frames per second on the Jetson AGX Xavier module.

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 YolactEdge: Real-time Instance Segmentation on the Edge

2021 ◽  
Author(s):  
Haotian Liu ◽  
Rafael A. Rivera Soto ◽  
Fanyi Xiao ◽  
Yong Jae Lee
Keyword(s):  
Real Time ◽  
Instance Segmentation

scholarly journals YOLACT++: Better Real-time Instance Segmentation

2020 ◽  
pp. 1-1 ◽  
Author(s):  
Daniel Bolya ◽  
Chong Zhou ◽  
Fanyi Xiao ◽  
Yong Jae Lee
Keyword(s):  
Real Time ◽  
Instance Segmentation

scholarly journals REAL-TIME DEEP NEURAL NETWORKS FOR MULTIPLE OBJECT TRACKING AND SEGMENTATION ON MONOCULAR VIDEO

2021 ◽  
Vol XLIV-2/W1-2021 ◽  
pp. 15-20
Author(s):  
I. Basharov ◽  
D. Yudin
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Deep Neural Networks ◽  
Unmanned Vehicles ◽  
Geometric Constraints ◽  
Found Objects ◽  
Cuda Technology ◽  
Monocular Video ◽  
Found Object ◽  
Instance Segmentation

Abstract. The paper is devoted to the task of multiple objects tracking and segmentation on monocular video, which was obtained by the camera of unmanned ground vehicle. The authors investigate various architectures of deep neural networks for this task solution. Special attention is paid to deep models providing inference in real time. The authors proposed an approach based on combining the modern SOLOv2 instance segmentation model, a neural network model for embedding generation for each found object, and a modified Hungarian tracking algorithm. The Hungarian algorithm was modified taking into account the geometric constraints on the positions of the found objects on the sequence of images. The investigated solution is a development and improvement of the state-of-the-art PointTrack method. The effectiveness of the proposed approach is demonstrated quantitatively and qualitatively on the popular KITTI MOTS dataset collected using the cameras of a driverless car. The software implementation of the approach was carried out. The acceleration of the procedure for the formation of a two-dimensional point cloud in the found image segment was done using the NVidia CUDA technology. At the same time, the proposed instance segmentation module provides a mean processing time of one image of 68 ms, the embedding and tracking module of 24 ms using the NVidia Tesla V100 GPU. This indicates that the proposed solution is promising for on-board computer vision systems for both unmanned vehicles and various robotic platforms.

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