Flying target detection and recognition by feature fusion

Kovács, Levente; Kovács, Anita; Utasi, Ákos; Szirányi, Tamás

doi:10.1117/1.oe.51.11.117002

Cited by 7 publications

(6 citation statements)

References 37 publications

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“…Thereafter, we fit to the 2-D side-view object silhouettes a convex hull, and a concave hull with 20cm resolution. Here the shape features are the contour vectors of the convex and concave hulls themselves, so that we store the contours of sample vehicles with various prototypes in a library, and we compare the contours of the detected objects to the library objects via the turning function based polygon representation [13]. …”

Section: Vehicle Model and Feature Extractionmentioning

confidence: 99%

A Model-Based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

Börcs

Nagy

Baticz

et al. 2015

Computer Vision - ACCV 2014 Workshops

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Abstract. Detection of vehicles in crowded 3-D urban scenes is a challenging problem in many computer vision related research fields, such as robot perception, autonomous driving, self-localization, and mapping. In this paper we present a model-based approach to solve the recognition problem from 3-D range data. In particular, we aim to detect and recognize vehicles from continuously streamed LIDAR point cloud sequences of a rotating multi-beam laser scanner. The end-to-end pipeline of our framework working on the raw streams of 3-D urban laser data consists of three steps 1) producing distinct groups of points which represent different urban objects 2) extracting reliable 3-D shape descriptors specifically designed for vehicles, considering the need for fast processing speed 3) executing binary classification on the extracted descriptors in order to perform vehicle detection. The extraction of our efficient shape descriptors provides a significant speedup with and increased detection accuracy compared to a PCA based 3-D bounding box fitting method used as baseline.

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Section: Vehicle Model and Feature Extractionmentioning

confidence: 99%

A Model-Based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

Börcs

Nagy

Baticz

et al. 2015

Computer Vision - ACCV 2014 Workshops

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“…For classification, the contour of the candidate is transformed into a rotation invariant tangent function representation [26]. To obtain a target class estimate, we propose a method based on [16], with a point of view of content based retrieval. Using the available labeled dataset, we construct an index structure [27] which indexes the dataset based on the comparison of the extracted shape descriptors (i.e., turning function representations).…”

Section: B Classificationmentioning

confidence: 99%

“…The proposed method produces a segmentation of the target from 2D passive ISAR images, based on previous results in saliency based feature map generation [14], [15]. First, we produce a fused feature map of directional and textural salient information, then we extract target regions and their contours as a basis for classification using shape based recognition and retrieval [16].…”

Section: Introductionmentioning

confidence: 99%

Automatic target classification in passive ISAR range-crossrange images

Manno-Kovacs

Giusti

Berizzi

et al. 2018

2018 IEEE Radar Conference (RadarConf18)

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Abstract-This paper presents a method for automatic analysis of passive radar 2D ISAR images to evaluate the possibilities and capabilities of image feature based target extraction and classification. The goal is to extend signal processing based detection and recognition methods with image information. The presented method is fast, easily embeddable and extendable, works near real-time, and we show its viability for classification using real passive 2D ISAR images.

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Section: Object Level Feature Extraction and Vehicle Recognitionmentioning

confidence: 99%

Dynamic 3D Environment Perception and Reconstruction Using a Mobile Rotating Multi-beam Lidar Scanner

Börcs

Nagy

Benedek

2015

Studies in Systems, Decision and Control

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In this chapter we introduce cooperating techniques for environment perception and reconstruction based on dynamic point cloud sequences of a single rotating multi-beam (RMB) Lidar sensor, which monitors the scene either from a moving vehicle top or from a static installed position. The joint aim of the addressed methods is to create 4D spatio-temporal models of large dynamic urban scenes containing various moving and static objects. Standalone RMB Lidar devices have been frequently applied in robot navigation tasks and proved to be efficient in moving object detection and recognition. However, they have not been widely exploited yet in video surveillance or dynamic virtual city modeling. We address here three different application areas of RMB Lidar measurements, starting from people activity analysis, through real time object perception for autonomous driving, until dynamic scene interpretation and visualization. First we introduce a multiple pedestrian tracking system with short term and long term person assignment steps. Second we present a model based real-time vehicle recognition approach. Third we propose techniques for geometric approximation of ground surfaces and building facades using the observed point cloud streams. This approach extracts simultaneously the reconstructed surfaces, motion information and objects from the registered dense point cloud completed with point time stamp information.

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Flying target detection and recognition by feature fusion

Cited by 7 publications

References 37 publications

A Model-Based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

A Model-Based Approach for Fast Vehicle Detection in Continuously Streamed Urban LIDAR Point Clouds

Automatic target classification in passive ISAR range-crossrange images

Dynamic 3D Environment Perception and Reconstruction Using a Mobile Rotating Multi-beam Lidar Scanner

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