You’ll NeverWalk Alone: Modeling Social Behavior for Multi-target Tracking
S. Pellegrini, A. Ess, K. Schindler and L. van Gool
ICCV 2009 (oral)
Abstract:
Object tracking typically relies on a dynamic model to predict the object’s location from its past trajectory. In crowded scenarios a strong dynamic model is particularly important, because more accurate predictions allow for smaller search regions, which greatly simplifies data association. Traditional dynamic models predict the location for each target solely based on its own history, without taking into account the remaining scene objects. Collisions are resolved only when they happen. Such an approach ignores important aspects of human behavior: people are driven by their future destination, take into account their environment, anticipate collisions, and adjust their trajectories at an early stage in order to avoid them. In this work, we introduce a model of dynamic social behavior, inspired by models developed for crowd simulation. The model is trained with videos recorded from birds-eye view at busy locations, and applied as a motion model for multi-people tracking from a vehicle-mounted camera. Experiments on real sequences show that accounting for social interactions and scene knowledge improves tracking performance, especially during occlusions. [PDF]
Tuesday, November 10, 2009
Saturday, November 07, 2009
Lab Meeting November 11, 2009(Jim Yu): Planning-based Prediction for Pedestrians
Title: Planning-based Prediction for Pedestrians
Author: B. D. Ziebart, N. Ratliff, G. Gallagher, C. Mertz, K. Peterson, J. A. Bagnell, M. Hebert, A K. Dey, S. Srinivasa
International Conference on Intelligent Robots and Systems (IROS 2009)
We present a novel approach for determining robot movements that efficiently accomplish the robot’s tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance sensitive robot trajectory planning provided by our approach.
Link
Author: B. D. Ziebart, N. Ratliff, G. Gallagher, C. Mertz, K. Peterson, J. A. Bagnell, M. Hebert, A K. Dey, S. Srinivasa
International Conference on Intelligent Robots and Systems (IROS 2009)
We present a novel approach for determining robot movements that efficiently accomplish the robot’s tasks while not hindering the movements of people within the environment. Our approach models the goal-directed trajectories of pedestrians using maximum entropy inverse optimal control. The advantage of this modeling approach is the generality of its learned cost function to changes in the environment and to entirely different environments. We employ the predictions of this model of pedestrian trajectories in a novel incremental planner and quantitatively show the improvement in hindrance sensitive robot trajectory planning provided by our approach.
Link
Friday, November 06, 2009
(PAMI2009)Head Pose Estimation in Computer Vision: A Survey
Authors:
Murphy-Chutorian, E.; Trivedi, M.M.;
Abstract:
The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.
Murphy-Chutorian, E.; Trivedi, M.M.;
Abstract:
The capacity to estimate the head pose of another person is a common human ability that presents a unique challenge for computer vision systems. Compared to face detection and recognition, which have been the primary foci of face-related vision research, identity-invariant head pose estimation has fewer rigorously evaluated systems or generic solutions. In this paper, we discuss the inherent difficulties in head pose estimation and present an organized survey describing the evolution of the field. Our discussion focuses on the advantages and disadvantages of each approach and spans 90 of the most innovative and characteristic papers that have been published on this topic. We compare these systems by focusing on their ability to estimate coarse and fine head pose, highlighting approaches that are well suited for unconstrained environments.
Wednesday, November 04, 2009
NTU talk: Video Analysis in Vision-Based Intelligent Systems
Title: Video Analysis in Vision-Based Intelligent Systems
Speaker: Prof. Hsu-Yung Cheng, National Central University
Time: 2:20pm, Nov 6 (Fri), 2009
Place: Room 103, CSIE building
Abstract: Computer vision and video analysis techniques play an important role in modern intelligent systems. Video-based systems can capture a larger variety of desired information and are relatively inexpensive because cameras are easy to install, operate, and maintain. With the huge amount of video cameras installed everywhere nowadays, there is an urgent need for automated video understanding techniques that can replace human operators to monitor the areas under surveillance. In this talk I will breifly introduce several topics and related techniques in intelligent surveillance applications. More discussions will be given on the topic of video object tracking. I will introduce a work on video object tracking which combines the advantages of both the flexibility of particle sampling and mathematical tractability of Kalman filters. Also, for objects that cannot be separated during the tracking proces, possible solutions are discussed.
Short Biography: Hsu-Yung Cheng received the Bachelor’s degree in computer science and information engineering from National Chiao-Tung University in Taiwan in 2000 and the Master’s degree from the same department in 2002. She earned a degree of Doctor of Philosophy from the University of Washington in Electrical Engineering in 2008. Hsu-Yung Cheng joined the Department of Computer Science and Information Engineering in National Central University in 2008 as an assistant professor. Her research interest includes image and video analysis and intelligent systems.
Speaker: Prof. Hsu-Yung Cheng, National Central University
Time: 2:20pm, Nov 6 (Fri), 2009
Place: Room 103, CSIE building
Abstract: Computer vision and video analysis techniques play an important role in modern intelligent systems. Video-based systems can capture a larger variety of desired information and are relatively inexpensive because cameras are easy to install, operate, and maintain. With the huge amount of video cameras installed everywhere nowadays, there is an urgent need for automated video understanding techniques that can replace human operators to monitor the areas under surveillance. In this talk I will breifly introduce several topics and related techniques in intelligent surveillance applications. More discussions will be given on the topic of video object tracking. I will introduce a work on video object tracking which combines the advantages of both the flexibility of particle sampling and mathematical tractability of Kalman filters. Also, for objects that cannot be separated during the tracking proces, possible solutions are discussed.
Short Biography: Hsu-Yung Cheng received the Bachelor’s degree in computer science and information engineering from National Chiao-Tung University in Taiwan in 2000 and the Master’s degree from the same department in 2002. She earned a degree of Doctor of Philosophy from the University of Washington in Electrical Engineering in 2008. Hsu-Yung Cheng joined the Department of Computer Science and Information Engineering in National Central University in 2008 as an assistant professor. Her research interest includes image and video analysis and intelligent systems.
CMU talk: Challenges in the Practical Application of Machine Learning
Intelligence Seminar
November 10, 2009
3:30 pm
Challenges in the Practical Application of Machine Learning
Carla E. Brodley, Tufts University
Abstract:
In this talk I will discuss the factors that impact the successful application of supervised machine learning. Driven by several interdisciplinary collaborations, we are addressing the problem of what to do when your initial accuracy is lower than is acceptable to your domain experts. Low accuracy can be due to three factors: noise in the class labels, insufficient training data, and whether the features describing each training example are able to discriminate the classes. In this talk, I will discuss research efforts at Tufts addressing the second two factors. The first project, introduces a new problem which we have named active class selection (ACS). ACS arises when one can ask the question: given the ability to collect n additional training instances, how should they be distributed with respect to class? The second project examines how one might assess that the class distinctions are not supported by the features and how constraint-based clustering can be used to uncover the true class structure of the data. These two issues and their solutions will be explored in the context of three applications. The first is to create a map of global map of the land cover of the Earth's surface from remotely sensed data (satellite data). The second is to build a classifier based on data collected from an "artificial nose" to discriminate vapors. The "nose" is a collection of sensors that have different reactions to different vapors. The third is to classify HRCT images of the lung.
Bio:
Carla E. Brodley is a professor in the Department of Computer Science at Tufts University. She received her PhD in computer science from the University of Massachusetts, at Amherst in 1994. From 1994-2004, she was on the faculty of the School of Electrical Engineering at Purdue University. Professor Brodley's research interests include machine learning, knowledge discovery in databases, and computer security. She has worked in the areas of anomaly detection, active learning, classifier formation, unsupervised learning, and applications of machine learning to remote sensing, computer security, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, saliva diagnostics, evidence-based medicine and chemistry. She was a member of the DSSG in 2004-2005. In 2001 she served as program co-chair for the International Conference on Machine Learning (ICML) and in 2004, she served as the general chair for ICML. Currently she is an associate editor of JMLR and Machine Learning, and she is on the editorial board of DKMD. She is a member of the AAAI Council and is co-chair of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W).
November 10, 2009
3:30 pm
Challenges in the Practical Application of Machine Learning
Carla E. Brodley, Tufts University
Abstract:
In this talk I will discuss the factors that impact the successful application of supervised machine learning. Driven by several interdisciplinary collaborations, we are addressing the problem of what to do when your initial accuracy is lower than is acceptable to your domain experts. Low accuracy can be due to three factors: noise in the class labels, insufficient training data, and whether the features describing each training example are able to discriminate the classes. In this talk, I will discuss research efforts at Tufts addressing the second two factors. The first project, introduces a new problem which we have named active class selection (ACS). ACS arises when one can ask the question: given the ability to collect n additional training instances, how should they be distributed with respect to class? The second project examines how one might assess that the class distinctions are not supported by the features and how constraint-based clustering can be used to uncover the true class structure of the data. These two issues and their solutions will be explored in the context of three applications. The first is to create a map of global map of the land cover of the Earth's surface from remotely sensed data (satellite data). The second is to build a classifier based on data collected from an "artificial nose" to discriminate vapors. The "nose" is a collection of sensors that have different reactions to different vapors. The third is to classify HRCT images of the lung.
Bio:
Carla E. Brodley is a professor in the Department of Computer Science at Tufts University. She received her PhD in computer science from the University of Massachusetts, at Amherst in 1994. From 1994-2004, she was on the faculty of the School of Electrical Engineering at Purdue University. Professor Brodley's research interests include machine learning, knowledge discovery in databases, and computer security. She has worked in the areas of anomaly detection, active learning, classifier formation, unsupervised learning, and applications of machine learning to remote sensing, computer security, digital libraries, astrophysics, content-based image retrieval of medical images, computational biology, saliva diagnostics, evidence-based medicine and chemistry. She was a member of the DSSG in 2004-2005. In 2001 she served as program co-chair for the International Conference on Machine Learning (ICML) and in 2004, she served as the general chair for ICML. Currently she is an associate editor of JMLR and Machine Learning, and she is on the editorial board of DKMD. She is a member of the AAAI Council and is co-chair of the Computing Research Association's Committee on the Status of Women in Computing Research (CRA-W).
Tuesday, November 03, 2009
Lab Meeting November 4, 2009(Chung-Han) : An Active Learning Approach for Segmenting Human Activity Datasets
Title: An Active Learning Approach for Segmenting Human Acticitiy Datasets
Author: Liyue Zhao, Gita Sukthankar
In: MM '09: Proceedings of the seventeen ACM international conference on Multimedia
Abtract:
Human activity datasets collected under natural conditions are an important source of data. Since these contain multiple activities in unscripted sequence, temporal segmentation of multimodal datasets is an important precursor to recognition and analysis. Manual segmentation is prohibitively time consuming and unsupervised approaches for segmentation are unreliable since they fail to exploit the semantic context of the data. Gathering labels for supervised learning places a large workload on the human user since it is relatively easy to gather a mass of unlabeled data but expensive to annotate. This paper proposes an active learning approach for segmenting large motion capture datasets with both small training sets and working sets. Support Vector Machines (SVMs) are learned using an active learning paradigm; after the classifiers are initialized with a small set of labeled data, the users are iteratively queried for labels as needed. We propose a novel method for initializing the classifiers, based on unsupervised segmentation and clustering of the dataset. By identifying and training the SVM with points from pure clusters, we can improve upon a random sampling strategy for creating the query set. Our active learning approach improves upon the initial unsupervised segmentation used to initialize the classifier, while requiring substantially less data than a fully supervised method; the resulting segmentation is comparable to the latter while requiring significantly less effort from the user.
[Full Text]
Author: Liyue Zhao, Gita Sukthankar
In: MM '09: Proceedings of the seventeen ACM international conference on Multimedia
Abtract:
Human activity datasets collected under natural conditions are an important source of data. Since these contain multiple activities in unscripted sequence, temporal segmentation of multimodal datasets is an important precursor to recognition and analysis. Manual segmentation is prohibitively time consuming and unsupervised approaches for segmentation are unreliable since they fail to exploit the semantic context of the data. Gathering labels for supervised learning places a large workload on the human user since it is relatively easy to gather a mass of unlabeled data but expensive to annotate. This paper proposes an active learning approach for segmenting large motion capture datasets with both small training sets and working sets. Support Vector Machines (SVMs) are learned using an active learning paradigm; after the classifiers are initialized with a small set of labeled data, the users are iteratively queried for labels as needed. We propose a novel method for initializing the classifiers, based on unsupervised segmentation and clustering of the dataset. By identifying and training the SVM with points from pure clusters, we can improve upon a random sampling strategy for creating the query set. Our active learning approach improves upon the initial unsupervised segmentation used to initialize the classifier, while requiring substantially less data than a fully supervised method; the resulting segmentation is comparable to the latter while requiring significantly less effort from the user.
[Full Text]
Monday, November 02, 2009
Lab Meeting November 4, 2009(Jimmy): Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer
Title: Learning To Detect Unseen Object Classes by Between-Class Attribute Transfer
Authors: Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling
In: CVPR2009
Abstract
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels.
In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new largescale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson’s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.
[link]
I will also try to introduce the NIPS2009 paper Zero-Shot Learning with Semantic Output Codes by M. Palatucci, D. Pomerleau, G. Hinton, and T.M. Mitchell, which gives some formalization to the problem.
Authors: Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling
In: CVPR2009
Abstract
We study the problem of object classification when training and test classes are disjoint, i.e. no training examples of the target classes are available. This setup has hardly been studied in computer vision research, but it is the rule rather than the exception, because the world contains tens of thousands of different object classes and for only a very few of them image, collections have been formed and annotated with suitable class labels.
In this paper, we tackle the problem by introducing attribute-based classification. It performs object detection based on a human-specified high-level description of the target objects instead of training images. The description consists of arbitrary semantic attributes, like shape, color or even geographic information. Because such properties transcend the specific learning task at hand, they can be pre-learned, e.g. from image datasets unrelated to the current task. Afterwards, new classes can be detected based on their attribute representation, without the need for a new training phase. In order to evaluate our method and to facilitate research in this area, we have assembled a new largescale dataset, “Animals with Attributes”, of over 30,000 animal images that match the 50 classes in Osherson’s classic table of how strongly humans associate 85 semantic attributes with animal classes. Our experiments show that by using an attribute layer it is indeed possible to build a learning object detection system that does not require any training images of the target classes.
[link]
I will also try to introduce the NIPS2009 paper Zero-Shot Learning with Semantic Output Codes by M. Palatucci, D. Pomerleau, G. Hinton, and T.M. Mitchell, which gives some formalization to the problem.
Wednesday, October 28, 2009
(IROS2009)Video: RF Vision: RFID Receive Signal Strength Indicator (RSSI) Images for Sensor Fusion and Mobile Manipulation
Video:
http://video.aol.co.uk/video-detail/rfid-receive-signal-strength-indicator-rssi-images/3237866515
Title: RF Vision: RFID Receive Signal Strength Indicator (RSSI) Images for Sensor Fusion and Mobile Manipulation
Abstract:
In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed.
Link:
IROS2009
I will find the pdf file later.
http://video.aol.co.uk/video-detail/rfid-receive-signal-strength-indicator-rssi-images/3237866515
Title: RF Vision: RFID Receive Signal Strength Indicator (RSSI) Images for Sensor Fusion and Mobile Manipulation
Abstract:
In this work we present a set of integrated methods that enable an RFID-enabled mobile manipulator to approach and grasp an object to which a self-adhesive passive (battery-free) UHF RFID tag has been affixed.
Link:
IROS2009
I will find the pdf file later.
Subscribe to:
Posts (Atom)
