Real-time object detection and localization with SIFT-based clustering

2012 ◽  
Vol 30 (8) ◽  
pp. 573-587 ◽  
Author(s):  
Paolo Piccinini ◽  
Andrea Prati ◽  
Rita Cucchiara
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection And Localization

scholarly journals Real-Time 3D Multi-Object Detection and Localization Based on Deep Learning for Road and Railway Smart Mobility

2021 ◽  
Vol 7 (8) ◽  
pp. 145
Author(s):  
Antoine Mauri ◽  
Redouane Khemmar ◽  
Benoit Decoux ◽  
Madjid Haddad ◽  
Rémi Boutteau
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Video Game ◽  
Autonomous Vehicles ◽  
Object Localization ◽  
Driver Assistance Systems ◽  
Smart Mobility ◽  
Bounding Boxes ◽  
Detection And Localization

For smart mobility, autonomous vehicles, and advanced driver-assistance systems (ADASs), perception of the environment is an important task in scene analysis and understanding. Better perception of the environment allows for enhanced decision making, which, in turn, enables very high-precision actions. To this end, we introduce in this work a new real-time deep learning approach for 3D multi-object detection for smart mobility not only on roads, but also on railways. To obtain the 3D bounding boxes of the objects, we modified a proven real-time 2D detector, YOLOv3, to predict 3D object localization, object dimensions, and object orientation. Our method has been evaluated on KITTI’s road dataset as well as on our own hybrid virtual road/rail dataset acquired from the video game Grand Theft Auto (GTA) V. The evaluation of our method on these two datasets shows good accuracy, but more importantly that it can be used in real-time conditions, in road and rail traffic environments. Through our experimental results, we also show the importance of the accuracy of prediction of the regions of interest (RoIs) used in the estimation of 3D bounding box parameters.

scholarly journals A New Dataset and Performance Evaluation of a Region-Based CNN for Urban Object Detection

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 301 ◽  
Author(s):  
Alex Dominguez-Sanchez ◽  
Miguel Cazorla ◽  
Sergio Orts-Escolano
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Autonomous Driving ◽  
Learning Techniques ◽  
Driving Assistance ◽  
Large Growth ◽  
Weakly Supervised ◽  
And Performance ◽  
Detection And Localization ◽  
Baseline Approach

In recent years, we have seen a large growth in the number of applications which use deep learning-based object detectors. Autonomous driving assistance systems (ADAS) are one of the areas where they have the most impact. This work presents a novel study evaluating a state-of-the-art technique for urban object detection and localization. In particular, we investigated the performance of the Faster R-CNN method to detect and localize urban objects in a variety of outdoor urban videos involving pedestrians, cars, bicycles and other objects moving in the scene (urban driving). We propose a new dataset that is used for benchmarking the accuracy of a real-time object detector (Faster R-CNN). Part of the data was collected using an HD camera mounted on a vehicle. Furthermore, some of the data is weakly annotated so it can be used for testing weakly supervised learning techniques. There already exist urban object datasets, but none of them include all the essential urban objects. We carried out extensive experiments demonstrating the effectiveness of the baseline approach. Additionally, we propose an R-CNN plus tracking technique to accelerate the process of real-time urban object detection.

Real-Time Object Detection and Localization in Compressive Sensed Video

2021 ◽  
Author(s):  
Yeshwanth Ravi Theja Bethi ◽  
Sathyaprakash Narayanan ◽  
Venkat Rangan ◽  
Anirban Chakraborty ◽  
Chetan Singh Thakur
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection And Localization

scholarly journals Object Color Estimation with Dominant Color in Automated Image Semantic Description Generation Tasks

2019 ◽  
pp. 90-97
Author(s):  
Aghasi Poghosyan
Keyword(s):  
Object Detection ◽  
Real Time ◽  
Detection System ◽  
Time Algorithm ◽  
Semantic Description ◽  
Image Tagging ◽  
Dominant Color ◽  
Color Name ◽  
Detection And Localization ◽  
Localization Systems

The automated image tagging is an important part of modern search engines. The generated image tags can be constructed from object names and their attributes, for example, colors. This work presents an object color name detection real-time algorithm. It is applicable to any automatic object detection and localization systems. The presented algorithm is fast enough to run after the existing real-time object detection system, without adding visible overhead. The algorithm uses k-means to detect the dominant color and selects the correct name for the color via Delta E (CIE 2000).

scholarly journals Deep Learning for Real-Time Capable Object Detection and Localization on Mobile Platforms

2017 ◽  
Vol 261 ◽  
pp. 012005 ◽  
Author(s):  
F. Particke ◽  
R. Kolbenschlag ◽  
M. Hiller ◽  
L. Patiño-Studencki ◽  
J. Thielecke
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Mobile Platforms ◽  
Detection And Localization

scholarly journals Implementation of a System for Real-Time Detection and Localization of Terrain Objects on Harvested Forest Land

Forests ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 1142
Author(s):  
Songyu Li ◽  
Håkan Lideskog
Keyword(s):  
Object Detection ◽  
Real Time ◽  
High Reliability ◽  
Full Range ◽  
Automatic Detection ◽  
Forest Land ◽  
Localization System ◽  
Real Time Detection ◽  
Detection And Localization ◽  
Camera Sensor

Research highlights: An automatic localization system for ground obstacles on harvested forest land based on existing mature hardware and software architecture has been successfully implemented. In the tested area, 98% of objects were successfully detected and could on average be positioned within 0.33 m from their true position in the full range 1–10 m from the camera sensor. Background and objectives: Forestry operations in forest environments are full of challenges; detection and localization of objects in complex forest terrains often require a lot of patience and energy from operators. Successful automatic real-time detection and localization of terrain objects not only can reduce the difficulty for operators but are essential for the automation of harvesting and logging tasks. We intend to implement a system prototype that can automatically locate ground obstacles on harvested forest land based on accessible hardware and common software infrastructure. Materials and Methods: An automatic object detection and localization system based on stereo camera sensing is described and evaluated in this paper. This demonstrated system detects and locates objects of interest automatically utilizing the YOLO (You Only Look Once) object detection algorithm and derivation of object positions in 3D space. System performance is evaluated by comparing the automatic detection results of the tests to manual labeling and positioning results. Results: Results show high reliability of the system for automatic detection and location of stumps and large stones and shows good potential for practical application. Overall, object detection on test tracks was 98% successful, and positional location errors were on average 0.33 m in the full range from 1–10 m from the camera sensor. Conclusions: The results indicate that object detection and localization can be used for better operator assessment of surroundings, as well as input to control machines and equipment for object avoidance or targeting.

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