Results 1 -
7 of
7
Spatio-temporal phrases for activity recognition computer vision
- In European Conference on Computer Vision (ECCV
, 2012
"... Abstract. The local feature based approaches have become popular for activity recognition. A local feature captures the local movement and appearance of a local region in a video, and thus can be ambiguous; e.g., it cannot tell whether a movement is from a person’s hand or foot, when the camera is f ..."
Abstract
-
Cited by 19 (2 self)
- Add to MetaCart
(Show Context)
Abstract. The local feature based approaches have become popular for activity recognition. A local feature captures the local movement and appearance of a local region in a video, and thus can be ambiguous; e.g., it cannot tell whether a movement is from a person’s hand or foot, when the camera is far away from the person. To better distinguish different types of activities, people have proposed using the combination of local features to encode the relationships of local movements. Due to the computation limit, previous work only creates a combination from neighboring features in space and/or time. In this paper, we propose an approach that efficiently identifies both local and long-range motion interactions; taking the “push ” activity as an example, our approach can capture the combination of the hand movement of one person and the foot response of another person, the local features of which are both spatially and temporally far away from each other. Our computational complexity is in linear time to the number of local features in a video. The extensive experiments show that our approach is generically effective for recognizing a wide variety of activities and activities spanning a long term, compared to a number of state-of-the-art methods.
Representing and Discovering Adversarial Team Behaviors using Player Roles
- in CVPR
, 2013
"... In this paper, we describe a method to represent and dis-cover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial be-havior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adver-saries, in add ..."
Abstract
-
Cited by 6 (5 self)
- Add to MetaCart
(Show Context)
In this paper, we describe a method to represent and dis-cover adversarial group behavior in a continuous domain. In comparison to other types of behavior, adversarial be-havior is heavily structured as the location of a player (or agent) is dependent both on their teammates and adver-saries, in addition to the tactics or strategies of the team. We present a method which can exploit this relationship through the use of a spatiotemporal basis model. As players constantly change roles during a match, we show that em-ploying a “role-based ” representation instead of one based on player “identity ” can best exploit the playing structure. As vision-based systems currently do not provide perfect de-tection/tracking (e.g. missed or false detections), we show that our compact representation can effectively “denoise” erroneous detections as well as enabling temporal anal-ysis, which was previously prohibitive due to the dimen-sionality of the signal. To evaluate our approach, we used a fully instrumented field-hockey pitch with 8 fixed high-definition (HD) cameras and evaluated our approach on approximately 200,000 frames of data from a state-of-the-art real-time player detector and compare it to manually labelled data. 1.
LEARNING FROM SEQUENTIAL DATA FOR ANOMALY DETECTION
, 2014
"... Anomaly detection has been used in a wide range of real world problems and has received significant attention in a number of research fields over the last decades. Anomaly detection attempts to identify events, activities, or observations which are measurably different than an expected behavior or p ..."
Abstract
- Add to MetaCart
(Show Context)
Anomaly detection has been used in a wide range of real world problems and has received significant attention in a number of research fields over the last decades. Anomaly detection attempts to identify events, activities, or observations which are measurably different than an expected behavior or pattern present in a dataset. This thesis focuses on a specific set of techniques targeting the detection of anomalous behavior in a discrete, symbolic, and sequential dataset. Since profiling complex sequential data is still an open problem in anomaly detection, and given that the rate of production of sequential data in fields ranging from finance to homeland security is exploding, there is a pressing need to develop effective detection algorithms that can handle patterns in sequential information flows. In this thesis, we address context-aware multi-class anomaly detection as applied to discrete sequences and develop a context learning approach using an unsupervised learning paradigm. We begin the anomaly detection process by applying our approach to differentiate normal behavior classes (contexts) before attempting to model normal
AUTOMATED CROWD BEHAVIOR ANALYSIS FOR VIDEO SURVEILLANCE APPLICATIONS
, 2012
"... Assist.Prof. Dr. Alptekin Temizel ..."
(Show Context)
Recognising Team Activities from Noisy Data
"... Recently, vision-based systems have been deployed in professional sports to track the ball and players to en-hance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be ..."
Abstract
- Add to MetaCart
(Show Context)
Recently, vision-based systems have been deployed in professional sports to track the ball and players to en-hance analysis of matches. Due to their unobtrusive nature, vision-based approaches are preferred to wearable sensors (e.g. GPS or RFID sensors) as it does not require players or balls to be instrumented prior to matches. Unfortunately, in continuous team sports where players need to be tracked continuously over long-periods of time (e.g. 35 minutes in field-hockey or 45 minutes in soccer), current vision-based tracking approaches are not reliable enough to provide fully automatic solutions. As such, human intervention is re-quired to fix-up missed or false detections. However, in in-stances where a human can not intervene due to the sheer amount of data being generated- this data can not be used due to the missing/noisy data. In this paper, we investigate two representations based on raw player detections (and not tracking) which are immune to missed and false detections. Specifically, we show that both team occupancy maps and centroids can be used to detect team activities, while the occupancy maps can be used to retrieve specific team ac-tivities. An evaluation on over 8 hours of field hockey data captured at a recent international tournament demonstrates the validity of the proposed approach. 1.
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. LMA: Location and Mobility Aware Medium Access Control Protocols for Vehicular Ad Hoc Networks
"... Abstract—In recent years, the incorporation of the directional antennas within mobile devices has been studied in many areas. The usage of directional antennas can greatly reduce the radio interference, which results in improved utilization of the wireless medium. It becomes practical to exploit the ..."
Abstract
- Add to MetaCart
(Show Context)
Abstract—In recent years, the incorporation of the directional antennas within mobile devices has been studied in many areas. The usage of directional antennas can greatly reduce the radio interference, which results in improved utilization of the wireless medium. It becomes practical to exploit the directional antennas in the Medium Access Control (MAC) protocol design. In this paper, a Location and Mobility Aware (LMA) MAC protocol is developed for the vehicular ad hoc networks. The predictive location and mobility of the vehicles are adopted to provide robust communication links while using the directional beams. The deafness problem is also alleviated using the directional listen (D-Listen) mechanism in the proposed algorithm. Moreover, the exploitation of the Directional Beacons (DBs) within the scheme can enhance the reliability of the communication linkages even the moving directions and speeds of the vehicles have been changed. Under dynamic moving scenarios, both the spatial reuse and the routing efficiency are preserved using the proposed LMA MAC scheme. The performance of the proposed algorithm is evaluated and compared with other existing MAC protocols in simulations. Index Terms—Vehicular ad hoc networks, medium access control, directional antennas, prediction mechanism. I.
