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Upload PDF. Follow this author. New articles by this author. New citations to this author. New articles related to this author's research. Email address for updates. My profile My library Metrics Alerts. Sign in. Get my own profile Cited by View all All Since Citations h-index 77 48 iindex In this paper, we therefore consider the problem of making best use of an object detector with a fixed and very small time budget.
The question we ask is: given a fixed time budget that allows for detector-based verification of k small regions-of-interest ROIs in the image, what are the best regions to attend to in order to obtain stable tracking performance? We address this problem by applying a statistical Poisson process model in order to rate the urgency by which individual ROIs should be attended to.
These ROIs are initially extracted from a 3D depth-based occupancy map of the scene and are then tracked over time. This allows us to balance the system resources in order to satisfy the twin goals of detecting newly appearing objects, while maintaining the quality of existing object trajectories. This paper proposes a pipeline for lying pose recognition from single images, which is designed for health-care robots to find fallen people. We firstly detect object bounding boxes by a mixture of viewpoint-specific part based model detectors and later estimate a detailed configuration of body parts on the detected regions by a finer tree-structured model.
Moreover, we exploit the information provided by detection to infer a reasonable limb prior for the pose estimation stage. Additional robustness is achieved by integrating a viewpointspecific foreground segmentation into the detection and body pose estimation stages. This step yields a refinement of detection scores and a better color model to initialize pose estimation. We apply our proposed approach to challenging data sets of fallen people in different scenarios.
Our quantitative and qualitative results demonstrate that the part-based model significantly outperforms a holistic model based on same feature type for lying pose detection. Moreover, our system offers a reasonable estimation for the body configuration of varying lying poses. In this paper, we address the problem of automatically detecting and tracking a variable number of persons in complex scenes using a monocular, potentially moving, uncalibrated camera.
We propose a novel approach for multi-person tracking-bydetection in a particle filtering framework.
International Journal of Computational Intelligence Systems
In addition to final high-confidence detections, our algorithm uses the continuous confidence of pedestrian detectors and online trained, instance-specific classifiers as a graded observation model. Thus, generic object category knowledge is complemented by instance-specific information. The main contribution of this paper is to explore how these unreliable information sources can be used for robust multi-person tracking. The algorithm detects and tracks a large number of dynamically moving persons in complex scenes with occlusions, does not rely on background modeling, requires no camera or ground plane calibration, and only makes use of information from the past.
Hence, it imposes very few restrictions and is suitable for online applications. Our experiments show that the method yields good tracking performance in a large variety of highly dynamic scenarios, such as typical surveillance videos, webcam footage, or sports sequences. We demonstrate that our algorithm outperforms other methods that rely on additional information. Furthermore, we analyze the influence of different algorithm components on the robustness. Pattern Anal. We present a real-time interactive 3D scanning system that allows users to scan complete object geometry by turning the object around in front of a real-time 3D range scanner.
The incoming 3D surface patches are registered and integrated into an online 3D point cloud.
- Segmentation and Tracking of Multiple Humans in Crowded Environments!
- A robust single and multiple moving object detection, tracking and classification - ScienceDirect?
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In contrast to previous systems the online reconstructed 3D model also serves as final result. Registration error accumulation which leads to the well-known loop closure problem is addressed already during the scanning session by distorting the object as rigidly as possible. Scanning errors are removed by explicitly handling outliers based on visibility constraints. Thus, no additional post-processing is required which otherwise might lead to artifacts in the model reconstruction. Both geometry and texture are used for registration which allows for a wide range of objects with different geometric and photometric properties to be scanned.
We show the results of our modeling approach on several difficult real-world objects. Qualitative and quantitative results are given for both synthetic and real data demonstrating the importance of online loop closure and outlier handling for model reconstruction. We show that our real-time scanning system has comparable accuracy to offline methods with the additional benefit of immediate feedback and results.
This paper addresses the task of efficient object class detection by means of the Hough transform.
Although ISM exhibits robust detection performance, its probabilistic formulation is unsatisfactory. It thereby gives a sound justification to the voting procedure and imposes minimal constraints. Both systems achieve state-of-the-art performance. Detections are found by gradient-based or branch and bound search, respectively.
It thereby avoids the unfavorable memory trade-off and any on-line pre-processing of the original Efficient Subwindow Search ESS. Finally, we show how to avoid soft-matching and spatial pyramid descriptors during detection without losing their positive effect. This makes algorithms simpler and faster. Both are possible if the object model is properly regularized and we discuss a modification of SVMs which allows for doing so.
Lehmann and Bastian Leibe and Luc J. In this paper, we present a real-time vision-based multiperson tracking system working in crowded urban environments. Our approach combines stereo visual odometry estimation, HOG pedestrian detection, and multi-hypothesis tracking-by-detection to a robust tracking framework that runs on a single laptop with a CUDA-enabled graphics card.
Through shifting the expensive computations to the GPU and making extensive use of scene geometry constraints we could build up a mobile system that runs with 10Hz. We experimentally demonstrate on several challenging sequences that our approach achieves competitive tracking performance. We systematically investigate how geometric constraints can be used for efficient sliding-window object detection. Starting with a general characterization of the space of sliding-window locations that correspond to geometrically valid object detections, we derive a general algorithm for incorporating ground plane constraints directly into the detector computation.
Our approach is indifferent to the choice of detection algorithm and can be applied in a wide range of scenarios.
In particular, it allows to effortlessly combine multiple different detectors and to automatically compute regions-of-interest for each of them. We demonstrate its potential in a fast CUDA implementation of the HOG detector and show that our algorithm enables a factor speed improvement on top of all other optimizations. Sudowe and B. In this paper, we propose two improvements to the popular Hough Forest object detection framework. We show how this framework can be extended to efficiently infer precise probabilistic segmentations for the object hypotheses and how those segmentations can be used to improve the final hypothesis selection.
Our approach benefits from the dense sampling of a Hough Forest detector, which results in qualitatively better segmentations than previous voting based methods. We show that, compared to previous approaches, the dense feature sampling necessitates several adaptations to the segmentation framework and propose an improved formulation. In addition, we propose an efficient cascaded voting scheme that significantly reduces the effort of the Hough voting stage without loss in accuracy.
Video Object Segmentation and Tracking: A Survey
We quantitatively evaluate our approach on several challenging sequences, reaching stateof-the-art performance and showing the effectiveness of the proposed framework. Classical tracking-by-detection approaches require a robust object detector that needs to be executed in each frame. However the detector is typically the most computationally expensive component, especially if more than one object class needs to be detected. In this paper we investigate how the usage of the object detector can be reduced by using stereo range data for following detected objects over time.
To this end we propose a hybrid tracking framework consisting of a stereo based ICP Iterative Closest Point tracker and a high-level multi-hypothesis tracker. Initiated by a detector response, the ICP tracker follows individual pedestrians over time using just the raw depth information. Its output is then fed into the high-level tracker that is responsible for solving long-term data association and occlusion handling. In addition, we propose to constrain the detector to run only on some small regions of interest ROIs that are extracted from a 3D depth based occupancy map of the scene.
We present experiments on real stereo sequences recorded from a moving camera setup in urban scenarios and show that our proposed approach achieves state of the art performance. Tracking with a moving camera is a challenging task due to the combined effects of scene activity and egomotion. As there is no longer a static image background from which moving objects can easily be distinguished, dedicated effort must be spent on detecting objects of interest in the input images and on determining their precise extent. In this chapter, we will give an overview of the main concepts and techniques used in such tracking-by-detection systems.
In detail, the chapter will present fundamental techniques and current state-of-the-art approaches for performing object detection, for obtaining detailed object segmentations from single images based on top—down and bottom—up cues, and for propagating this information over time. The visual recognition problem is central to computer vision research.
From robotics to information retrieval, many desired applications demand the ability to identify and localize categories, places, and objects. This tutorial overviews computer vision algorithms for visual object recognition and image classification. We introduce primary representations and learning approaches, with an emphasis on recent advances in the field.
The target audience consists of researchers or students working in AI, robotics, or vision who would like to understand what methods and representations are available for these problems. This lecture summarizes what is and isn't possible to do reliably today, and overviews key concepts that could be employed in systems requiring visual categorization. A motion estimating device first detects mobile objects Oi and Oi' in continuous image frames T and T', and acquires image areas Ri and Ri' corresponding to the mobile objects Oi and Oi'.
Then, the motion estimating device removes the image areas Ri and Ri' corresponding to the mobile objects Oi and Oi' in the image frames T and T', extracts corresponding point pairs Pj of feature points between the image frames T and T' from the image areas having removed the image areas Ri and Ri', and carries out the motion estimation of the autonomous mobile machine between the image frames T and T' on the basis of the positional relationship of the corresponding point pairs Pj of feature points.
We address the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform.