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On Detection of Multiple Object Instances Using Hough Transforms
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010Hough transform-based methods for detecting multiple objects use nonmaxima suppression or mode seeking to locate and distinguish peaks in Hough images. Such postprocessing requires the tuning of many parameters and is often fragile, especially when objects are located spatially close to each other.
Olga, Barinova +2 more
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Multiple-part based Pedestrian Detection using Interfering Object Detection
Third International Conference on Natural Computation (ICNC 2007), 2007We propose an improved pedestrian detection framework based on Viola's Adaboost cascade framework, and its improvements focus on the three aspects: First we use edgelet features in addition to the haar-like features to capture more information about the pedestrian contours. Second we design a fast algorithm to combine the multiple-part detectors. Third
Xin Mao, Feihu Qi, Wenjia Zhu
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Object Detection with Multiple Motion Models
2010Existing joint detection and tracking algorithms generally assume one single motion model for objects of interest However, in real world many objects have more than one motion model In this paper we present a joint detection and tracking algorithm that is able to detect objects with multiple motion models For such an object, a discrete variable is ...
Zhijie Wang, Hong Zhang
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Adaptive codebook modeling based multiple objects detection
2018 Chinese Control And Decision Conference (CCDC), 2018As a hot topic in the field of computer vision, object detection has been widely used in many practical applications, such as intelligent transportation, intelligent video surveillance. A novel multi-target detection algorithm based on YUV color space is here proposed to solve the existing issues of illumination changes and background changes. Firstly,
Bowen Lu +3 more
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Multiple Object Detection using Deep Learning
2021 3rd International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), 2021Sujeet Kumar +4 more
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Object Detection Using Multiple Level Annotations
2019Object detection is a fundamental problem in computer vision. Impressive results have been achieved on large-scale detection benchmarks by fully-supervised object detection (FSOD) methods. However, FSOD approaches require tremendous instance-level annotations, which are time-consuming to collect.
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A detection-based multiple object tracking method
2004 International Conference on Image Processing, 2004. ICIP '04., 2005In this paper we describe a method for tracking multiple objects whose number is unknown and varies during tracking. Based on preliminary results of object detection in each image which may have missing and/or false detection, the multiple object tracking method keeps a graph structure where it maintains multiple hypotheses about the number and the ...
null Mei Han +3 more
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Detecting abandoned objects using interacting multiple models
SPIE Proceedings, 2015In recent years, the wide use of video surveillance systems has caused an enormous increase in the amount of data that has to be stored, monitored, and processed. As a consequence, it is crucial to support human operators with automated surveillance applications.
Stefan Becker +4 more
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Detection of Multiple Instances of Video Objects
2011 Seventh International Conference on Signal Image Technology & Internet-Based Systems, 2011This paper tackles the issue of retrieving different instances of an object of interest within a given video document or in a video database. The principle consists of considering a semi-global image representation based on an over-segmentation of image frames.
Andrei Bursuc +2 more
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