Components for Object Detection and Identification

2006 ◽  
pp. 225-237 ◽  
Author(s):  
Bernd Heisele ◽  
Ivaylo Riskov ◽  
Christian Morgenstern
Keyword(s):  
Object Detection ◽  
Detection And Identification

Object Detection Using an Ideal Observer Model

2020 ◽  
Vol 2020 (16) ◽  
pp. 41-1-41-7
Author(s):  
Orit Skorka ◽  
Paul J. Kane
Keyword(s):  
Object Detection ◽  
Matched Filter ◽  
Image Sensors ◽  
Ideal Observer ◽  
Noise Power ◽  
Relative Evaluation ◽  
Detection And Identification ◽  
Cross Correlation Analysis ◽  
Observer Model ◽  
The Ideal

Many of the metrics developed for informational imaging are useful in automotive imaging, since many of the tasks – for example, object detection and identification – are similar. This work discusses sensor characterization parameters for the Ideal Observer SNR model, and elaborates on the noise power spectrum. It presents cross-correlation analysis results for matched-filter detection of a tribar pattern in sets of resolution target images that were captured with three image sensors over a range of illumination levels. Lastly, the work compares the crosscorrelation data to predictions made by the Ideal Observer Model and demonstrates good agreement between the two methods on relative evaluation of detection capabilities.

LiDAR-Camera Fusion for 3D Object Detection

2020 ◽  
Vol 2020 (16) ◽  
pp. 257-1-257-9
Author(s):  
Darshan Bhanushali ◽  
Robert Relyea ◽  
Karan Manghi ◽  
Abhishek Vashist ◽  
Clark Hochgraf ◽  
...  
Keyword(s):  
Object Detection ◽  
Autonomous Agents ◽  
Situational Awareness ◽  
Point Clouds ◽  
Fusion Model ◽  
Distance Measurements ◽  
Learning Framework ◽  
Detection And Identification ◽  
Agent Interaction ◽  
3D Object Detection

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset.

scholarly journals Editorial for Special Issue “Remote Sensing for Target Object Detection and Identification”

2020 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Gemine Vivone ◽  
Paolo Addesso ◽  
Amanda Ziemann
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Unmanned Aerial Vehicles ◽  
Target Object ◽  
Special Issue ◽  
Remotely Sensed Data ◽  
Aerial Vehicles ◽  
Detection And Identification ◽  
Different Types ◽  
Identification Techniques

This special issue gathers fourteen papers focused on the application of a variety of target object detection and identification techniques for remotely-sensed data. These data are acquired by different types of sensors (both passive and active) and are located on various platforms, ranging from satellites to unmanned aerial vehicles. This editorial provides an overview of the contributed papers, briefly presenting the technologies and algorithms employed as well as the related applications.

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