Results 1 - 10
of
35
P.: Multi-camera people tracking with a probabilistic occupancy map
- IEEE Transactions on Pattern Analysis and Machine Intelligence
, 2007
"... Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In ..."
Abstract
-
Cited by 152 (11 self)
- Add to MetaCart
(Show Context)
Given three or four synchronized videos taken at eye level and from different angles, we show that we can effectively combine a generative model with dynamic programming to accurately follow up to six individuals across thousands of frames in spite of significant occlusions and lighting changes. In addition, we also derive metrically accurate trajectories for each one of them. Our contribution is twofold. First, we demonstrate that our generative model can effectively handle occlusions in each time frame independently, even when the only data available comes from the output of a simple background subtraction algorithm and when the number of individuals is unknown a priori. Second, we show that multi-person tracking can be reliably achieved by processing individual trajectories separately over long sequences, provided that a reasonable heuristic is used to rank these individuals and avoid confusing them with one another. Figure 1: Images from two indoor and two outdoor multi-camera video sequences we use for our experiments. At each time step, we draw a box around people we detect and assign to them an Id number that follows them throughout the sequence. 1
Appearance Models for Occlusion Handling
- 2nd IEEE Workshop on Performance Evaluation of Tracking and Surveillance
, 2001
"... Objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. In this paper we present a method of tracking objects through ..."
Abstract
-
Cited by 78 (11 self)
- Add to MetaCart
(Show Context)
Objects in the world exhibit complex interactions. When captured in a video sequence, some interactions manifest themselves as occlusions. A visual tracking system must be able to track objects which are partially or even fully occluded. In this paper we present a method of tracking objects through occlusions using appearance models. These models are used to localize objects during partial occlusions, detect complete occlusions and resolve depth ordering of objects during occlusions. This paper presents a tracking system which successfully deals with complex real world interactions, as demonstrated on the PETS 2001 dataset. 1.
Tracking Multiple People with a Multi-Camera System
- IEEE Workshop on Multi-Object Tracking
, 2001
"... We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment. Bayesian networks are used to combine multiple modalities for matching subjects between consecutive image frames and between multiple camera views. Unlike other occlusion reasoning m ..."
Abstract
-
Cited by 45 (0 self)
- Add to MetaCart
(Show Context)
We present a multi-camera system based on Bayesian modality fusion to track multiple people in an indoor environment. Bayesian networks are used to combine multiple modalities for matching subjects between consecutive image frames and between multiple camera views. Unlike other occlusion reasoning methods, we use multiple cameras in order to obtain continuous visual information of people in either or both cameras so that they can be tracked through interactions. Results demonstrate that the system can maintain people’s identities by using multiple cameras cooperatively. 1.
Enabling Video Privacy through Computer Vision
- IEEE Security & Privacy Magazine
, 2005
"... LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its ..."
Abstract
-
Cited by 33 (2 self)
- Add to MetaCart
(Show Context)
LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g., payment of royalties). Copies may be requested from IBM T. J. Watson Research Center, P.
Simultaneous Tracking of Multiple Body Parts of Interacting Persons
- Computer Vision and Image Understanding
, 2006
"... ..."
Tracking multiple objects through occlusions
- In CVPR
, 2005
"... All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
Abstract
-
Cited by 24 (0 self)
- Add to MetaCart
(Show Context)
All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Voting-based simultaneous tracking of multiple video objects.
- Proceeding of the SPIE International Conference on Image and Video Communications and Processing,
, 2003
"... ..."
Temporal spatio-velocity transform and its application to tracking and interaction
- Computer Vision and Image Understanding
"... Copyright by ..."
Moving Object Detection, Tracking and Classification for Smart Video Surveillance
, 2004
"... Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, m ..."
Abstract
-
Cited by 11 (0 self)
- Add to MetaCart
Video surveillance has long been in use to monitor security sensitive areas such as banks, department stores, highways, crowded public places and borders. The advance in computing power, availability of large-capacity storage devices and high speed network infrastructure paved the way for cheaper, multi sensor video surveillance systems. Traditionally, the video outputs are processed online by human operators and are usually saved to tapes for later use only after a forensic event. The increase in the number of cameras in ordinary surveillance systems overloaded both the human operators and the storage devices with high volumes of data and made it infeasible to ensure proper monitoring of sensitive areas for long times. In order to filter out redundant information generated by an array of cameras, and increase the response time to forensic events, assisting the human operators with identification of important events in video by the use of "smart" video surveillance systems has become a critical requirement. The making of video surveillance systems "smart" requires fast, reliable and robust algorithms for moving object detection, classification, tracking and activity analysis. In this
MULTIPLE OBJECT TRACKING WITH OCCLUSIONS USING HOG DESCRIPTORS AND MULTI RESOLUTION IMAGES
"... We present a multiple object tracking algorithm working with occlusions. Firstly, for each detected object we compute feature points using FAST algorithm [1]. Secondly, for each feature point we build a descriptor based on Histogram of Oriented Gradients (HOG) [2]. Thirdly, we track feature points u ..."
Abstract
-
Cited by 7 (2 self)
- Add to MetaCart
(Show Context)
We present a multiple object tracking algorithm working with occlusions. Firstly, for each detected object we compute feature points using FAST algorithm [1]. Secondly, for each feature point we build a descriptor based on Histogram of Oriented Gradients (HOG) [2]. Thirdly, we track feature points using these descriptors. Object tracking is possible even if objects are occluded. If few objects are merged and detected as a single one, we assign newly detected feature points in such single object to one of these occluded objects. We apply a probabilistic method for this task using information from the previous frames like object size and motion (speed and orientation). We use multi resolution images to decrease the processing time. Our approach is tested on the synthetic video sequence, the KTH dataset [3] and the CAVIAR dataset [4]. All tests confirm the effectiveness of our approach. 1
