scholarly journals THE OBJECT TRACKING ALGORITHM USING DIMENSIONAL BASED DETECTION FOR PUBLIC STREET ENVIRONMENT

2020 ◽  
Vol 17 (2) ◽  
pp. 123-127
Author(s):  
I. G. Matveev ◽  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Mesh Network ◽  
Detection Accuracy ◽  
Low Performance ◽  
Computational Resources ◽  
World Environment ◽  
Street Environment

The paper proposes an approach to object tracking for public street environments using dimensional based object detection algorithm. Besides the tracking functionality, the proposed algorithm improves the detection accuracy of the dimensional based object detection algorithm. The proposed tracking approach uses detection information obtained from multiple cameras which are structured as a mesh network. Conducted experiments performed in a real-world environment have shown 10 to 40 percent higher detection accuracy that has proved the proposed concept. The tracking algorithm requires negligible computational resources that make the algorithm especially applicable for low-performance Internet of things infrastructure.

scholarly journals Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility

2020 ◽  
Vol 18 (1) ◽  
pp. 91
Author(s):  
Louis Lecrosnier ◽  
Redouane Khemmar ◽  
Nicolas Ragot ◽  
Benoit Decoux ◽  
Romain Rossi ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Indoor Environment ◽  
Distance Estimation ◽  
Depth Estimation ◽  
Detection Algorithm ◽  
Use Case ◽  
Detection Distance ◽  
Detection Depth ◽  
Sort Algorithm

This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an adaptation of the YOLOv3 object detection algorithm to our use case. Then, we present our depth estimation approach using an Intel RealSense camera. Finally, as a third and last step of our approach, we present our 3D object tracking approach based on the SORT algorithm. In order to validate all the developments, we have carried out different experiments in a controlled indoor environment. Detection, distance estimation and object tracking are experimented using our own dataset, which includes doors and door handles.

scholarly journals A Set of Single YOLO Modalities to Detect Occluded Entities via Viewpoint Conversion

2021 ◽  
Vol 11 (13) ◽  
pp. 6016
Author(s):  
Jinsoo Kim ◽  
Jeongho Cho
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Cloud Data ◽  
Detection Techniques ◽  
Bounding Boxes ◽  
Partially Occluded ◽  
Rgb Image

For autonomous vehicles, it is critical to be aware of the driving environment to avoid collisions and drive safely. The recent evolution of convolutional neural networks has contributed significantly to accelerating the development of object detection techniques that enable autonomous vehicles to handle rapid changes in various driving environments. However, collisions in an autonomous driving environment can still occur due to undetected obstacles and various perception problems, particularly occlusion. Thus, we propose a robust object detection algorithm for environments in which objects are truncated or occluded by employing RGB image and light detection and ranging (LiDAR) bird’s eye view (BEV) representations. This structure combines independent detection results obtained in parallel through “you only look once” networks using an RGB image and a height map converted from the BEV representations of LiDAR’s point cloud data (PCD). The region proposal of an object is determined via non-maximum suppression, which suppresses the bounding boxes of adjacent regions. A performance evaluation of the proposed scheme was performed using the KITTI vision benchmark suite dataset. The results demonstrate the detection accuracy in the case of integration of PCD BEV representations is superior to when only an RGB camera is used. In addition, robustness is improved by significantly enhancing detection accuracy even when the target objects are partially occluded when viewed from the front, which demonstrates that the proposed algorithm outperforms the conventional RGB-based model.

scholarly journals GC-YOLOv3: You Only Look Once with Global Context Block

Electronics ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1235
Author(s):  
Yang Yang ◽  
Hongmin Deng
Keyword(s):  
Object Detection ◽  
Irrelevant Information ◽  
Detection Algorithm ◽  
Visual Object ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Average Precision ◽  
Global Context ◽  
Pascal Voc ◽  
Feature Pyramid

In order to make the classification and regression of single-stage detectors more accurate, an object detection algorithm named Global Context You-Only-Look-Once v3 (GC-YOLOv3) is proposed based on the You-Only-Look-Once (YOLO) in this paper. Firstly, a better cascading model with learnable semantic fusion between a feature extraction network and a feature pyramid network is designed to improve detection accuracy using a global context block. Secondly, the information to be retained is screened by combining three different scaling feature maps together. Finally, a global self-attention mechanism is used to highlight the useful information of feature maps while suppressing irrelevant information. Experiments show that our GC-YOLOv3 reaches a maximum of 55.5 object detection mean Average Precision (mAP)@0.5 on Common Objects in Context (COCO) 2017 test-dev and that the mAP is 5.1% higher than that of the YOLOv3 algorithm on Pascal Visual Object Classes (PASCAL VOC) 2007 test set. Therefore, experiments indicate that the proposed GC-YOLOv3 model exhibits optimal performance on the PASCAL VOC and COCO datasets.

scholarly journals An Improved Object Detection Method using Feature Map Refinement and Anchor Optimization

2021 ◽  
Vol 15 ◽  
pp. 541-549
Author(s):  
Yuxia Wang ◽  
Wenzhu Yang ◽  
Tongtong Yuan ◽  
Qian Li
Keyword(s):  
Object Detection ◽  
Detection Method ◽  
Search Space ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Semantic Features ◽  
Feature Map ◽  
Input Size ◽  
Fusion Operation ◽  
Different Levels

Lower detection accuracy and insufficient detection ability for small objects are the main problems of the region-free object detection algorithm. Aiming at solving the abovementioned problems, an improved object detection method using feature map refinement and anchor optimization is proposed. Firstly, the reverse fusion operation is performed on each of the object detection layer, which can provide the lower layers with more semantic information by the fusion of detection features at different levels. Secondly, the self-attention module is used to refine each detection feature map, calibrates the features between channels, and enhances the expression ability of local features. In addition, the anchor optimization model is introduced on each feature layer associated with anchors, and the anchors with higher probability of containing an object and more closely match the location and size of the object are obtained. In this model, semantic features are used to confirm and remove negative anchors to reduce search space of the objects, and preliminary adjustments are made to the locations and sizes of anchors. Comprehensive experimental results on PASCAL VOC detection dataset demonstrate the effectiveness of the proposed method. In particular, with VGG-16 and lower dimension 300×300 input size, the proposed method achieves a mAP of 79.1% on VOC 2007 test set with an inference speed of 24.7 milliseconds per image.

scholarly journals A New Tracking Algorithm Based on Improved Fuzzy C-Means Dynamic Kalman Filter in Sport Video

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yonghua Zhou ◽  
Ting Huang ◽  
Shangbin Li
Keyword(s):  
Kalman Filter ◽  
Target Function ◽  
Detection Algorithm ◽  
Tracking Algorithm ◽  
Contextual Cueing ◽  
Detection Accuracy ◽  
Threshold Strategy ◽  
Fuzzy C Means ◽  
Motion State ◽  
Sport Video

The paper studied the problems of soccer detection and tracking in soccer tracking, in soccer detection; as the size of the soccer is too small to extract distinguishable feature, it is difficult to detect the soccer automatically. To solve this problem, a soccer detection algorithm was based on class weighted spatial Fuzzy C-means (ws-FCM) was proposed. Firstly, the target function of the spatial Fuzzy C-means was improved. Subsequently, a bi-threshold strategy was proposed to detect the soccer automatically. In the aspect of soccer tracking, existing methods fail to detect the soccer when it was occluded by several players successively. To solve this problem, the motion state of soccer of broadcast soccer video was analyzed, which is inspired by the contextual cueing effect of human visual search. According to the motion state of the soccer, parameters updating function of dynamic Kalman filter (DKF) were improved. Thus, a soccer tracking algorithm based on multiple search regions dynamic Kalman filter (MDKF) was proposed, which enhances the robustness of soccer tracking by extending the search area. The experiments show that the proposed algorithm can automatically detect soccer in images with high detection accuracy and can track the soccer more robustly, with better occlusion handle ability.

scholarly journals CXR-RefineDet: Single-Shot Refinement Neural Network for Chest X-Ray Radiograph Based on Multiple Lesions Detection

2022 ◽  
Vol 2022 ◽  
pp. 1-11
Author(s):  
Cong Lin ◽  
Yongbin Zheng ◽  
Xiuchun Xiao ◽  
Jialun Lin
Keyword(s):  
Neural Network ◽  
Object Detection ◽  
Disease Diagnosis ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Single Shot ◽  
Detection Model ◽  
Network Output ◽  
Artificial Intelligence Technology ◽  
Chest X Ray

The workload of radiologists has dramatically increased in the context of the COVID-19 pandemic, causing misdiagnosis and missed diagnosis of diseases. The use of artificial intelligence technology can assist doctors in locating and identifying lesions in medical images. In order to improve the accuracy of disease diagnosis in medical imaging, we propose a lung disease detection neural network that is superior to the current mainstream object detection model in this paper. By combining the advantages of RepVGG block and Resblock in information fusion and information extraction, we design a backbone RRNet with few parameters and strong feature extraction capabilities. After that, we propose a structure called Information Reuse, which can solve the problem of low utilization of the original network output features by connecting the normalized features back to the network. Combining the network of RRNet and the improved RefineDet, we propose the overall network which was called CXR-RefineDet. Through a large number of experiments on the largest public lung chest radiograph detection dataset VinDr-CXR, it is found that the detection accuracy and inference speed of CXR-RefineDet have reached 0.1686 mAP and 6.8 fps, respectively, which is better than the two-stage object detection algorithm using a strong backbone like ResNet-50 and ResNet-101. In addition, the fast reasoning speed of CXR-RefineDet also provides the possibility for the actual implementation of the computer-aided diagnosis system.

A Robust Detector for Automated Welding Seam Tracking System

2021 ◽  
Vol 143 (7) ◽  
Author(s):  
Yanbiao Zou ◽  
Mingquan Zhu ◽  
Xiangzhi Chen
Keyword(s):  
Object Detection ◽  
Weld Seam ◽  
Tracking System ◽  
Welding Current ◽  
Detection Algorithm ◽  
Seam Tracking ◽  
Detection Accuracy ◽  
Single Shot ◽  
Welding Seam ◽  
Laser Vision

Abstract Accurate locating of the weld seam under strong noise is the biggest challenge for automated welding. In this paper, we construct a robust seam detector on the framework of deep learning object detection algorithm. The representative object algorithm, a single shot multibox detector (SSD), is studied to establish the seam detector framework. The improved SSD is applied to seam detection. Under the SSD object detection framework, combined with the characteristics of the seam detection task, the multifeature combination network (MFCN) is proposed. The network comprehensively utilizes the local information and global information carried by the multilayer features to detect a weld seam and realizes the rapid and accurate detection of the weld seam. To solve the problem of single-frame seam image detection algorithm failure under continuous super-strong noise, the sequence image multifeature combination network (SMFCN) is proposed based on the MFCN detector. The recurrent neural network (RNN) is used to learn the temporal context information of convolutional features to accurately detect the seam under continuous super-noise. Experimental results show that the proposed seam detectors are extremely robust. The SMFCN can maintain extremely high detection accuracy under continuous super-strong noise. The welding results show that the laser vision seam tracking system using the SMFCN can ensure that the welding precision meets industrial requirements under a welding current of 150 A.

Object Tracking Algorithm based on Improved Siamese Convolutional Networks Combined with Deep Contour Extraction and Object Detection Under Airborne Platform

2020 ◽  
Vol 64 (4) ◽  
pp. 40409-1-40409-11
Author(s):  
Xiuyan Tian ◽  
Haifang Li ◽  
Hongxia Deng
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Clustering Algorithm ◽  
Object Perception ◽  
Tracking Algorithm ◽  
Tracking Performance ◽  
Contour Extraction ◽  
Data Set ◽  
Siamese Network ◽  
Detection And Tracking

Abstract Object detection and tracking is an indispensable module in airborne optoelectronic equipment, and its detection and tracking performance is directly related to the accuracy of object perception. Recently, the improved Siamese network tracking algorithm has achieved excellent results on various challenging data sets. However, most of the improved algorithms use local fixed search strategies, which cannot update the template. In addition, the template will introduce background interference, which will lead to tracking drift and eventually cause tracking failure. In order to solve these problems, this article proposes an improved fully connected Siamese tracking algorithm combined with object contour extraction and object detection, which uses the contour template of the object instead of the bounding-box template to reduce the background clutter interference. First, the contour detection network automatically obtains the closed contour information of the object and uses the flood-filling clustering algorithm to obtain the contour template. Then, the contour template and the search area are fed into the improved Siamese network to obtain the optimal tracking score value and adaptively update the contour template. If the object is fully obscured or lost, the YoLo v3 network is used to search the object in the entire field of view to achieve stable tracking throughout the process. A large number of qualitative and quantitative simulation results on benchmark test data set and the flying data set show that the improved model can not only improve the object tracking performance under complex backgrounds, but also improve the response time of airborne systems, which has high engineering application value.

scholarly journals Parallel tracking and detection for long-term object tracking

2020 ◽  
Vol 17 (2) ◽  
pp. 172988142090257
Author(s):  
Dan Xiong ◽  
Huimin Lu ◽  
Qinghua Yu ◽  
Junhao Xiao ◽  
Wei Han ◽  
...  
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Detection Algorithm ◽  
Detection Algorithms ◽  
Art Object ◽  
High Processing Speed ◽  
Online Learning Algorithms ◽  
Integrate State ◽  
Unknown Object

High tracking frame rates have been achieved based on traditional tracking methods which however would fail due to drifts of the object template or model, especially when the object disappears from the camera’s field of view. To deal with it, tracking-and-detection-combination has become more and more popular for long-term unknown object tracking, whose detector almost does not drift and can regain the disappeared object when it comes back. However, for online machine learning and multiscale object detection, expensive computing resources and time are required. So it is not a good idea to combine tracking and detection sequentially like Tracking-Learning-Detection algorithm. Inspired from parallel tracking and mapping, this article proposes a framework of parallel tracking and detection for unknown object tracking. The object tracking algorithm is split into two separate tasks—tracking and detection which can be processed in two different threads, respectively. One thread is used to deal with the tracking between consecutive frames with a high processing speed. The other thread runs online learning algorithms to construct a discriminative model for object detection. Using our proposed framework, high tracking frame rates and the ability of correcting and recovering the failed tracker can be combined effectively. Furthermore, our framework provides open interfaces to integrate state-of-the-art object tracking and detection algorithms. We carry out an evaluation of several popular tracking and detection algorithms using the proposed framework. The experimental results show that different tracking and detection algorithms can be integrated and compared effectively by our proposed framework, and robust and fast long-term object tracking can be realized.

scholarly journals A Real-Time Object Detector for Autonomous Vehicles Based on YOLOv4

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Rui Wang ◽  
Ziyue Wang ◽  
Zhengwei Xu ◽  
Chi Wang ◽  
Qiang Li ◽  
...  
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Speed ◽  
Feature Fusion ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Model Parameters ◽  
Detection Accuracy ◽  
Time Operation ◽  
On The Road

Object detection is an important part of autonomous driving technology. To ensure the safe running of vehicles at high speed, real-time and accurate detection of all the objects on the road is required. How to balance the speed and accuracy of detection is a hot research topic in recent years. This paper puts forward a one-stage object detection algorithm based on YOLOv4, which improves the detection accuracy and supports real-time operation. The backbone of the algorithm doubles the stacking times of the last residual block of CSPDarkNet53. The neck of the algorithm replaces the SPP with the RFB structure, improves the PAN structure of the feature fusion module, adds the attention mechanism CBAM and CA structure to the backbone and neck structure, and finally reduces the overall width of the network to the original 3/4, so as to reduce the model parameters and improve the inference speed. Compared with YOLOv4, the algorithm in this paper improves the average accuracy on KITTI dataset by 2.06% and BDD dataset by 2.95%. When the detection accuracy is almost unchanged, the inference speed of this algorithm is increased by 9.14%, and it can detect in real time at a speed of more than 58.47 FPS.

Sign in / Sign up

Export Citation Format

Share Document