Corporate Donors
Tuesday, Aug. 30
-
Smart Resource-aware Multimedia Sensor Network for Automatic Detection of Complex Events
Abstract
This paper presents a smart resource-aware multimedia sensor network. We illustrate a surveillance system which supports human operators, by automatically detecting the complex events and giving the possibility to recall the detected events and searching them in an intelligent search engine. Four subsystems have been implemented, the tracking and detection system, the network configuration system, the reasoning system and an advanced archiving system in an annotated multimedia database.
Optimizing Mean Reciprocal Rank for Person Re-identification
Abstract
Person re-identification is one of the most challenging is- sues in network-based surveillance. The difficulties mainly come from the great appearance variations induced by il- lumination, camera view and body pose changes. Maybe influenced by the research on face recognition and gen- eral object recognition, this problem is habitually treated as a verification or classification problem, and much effort has been put on optimizing standard recognition criteria. However, we found that in practical applications the users usually have different expectations. For example, in a real surveillance system, we may expect that a visual user inter- face can show us the relevant images in the first few (e.g. 20) candidates, but not necessarily before all the irrelevant ones. In other words, there is no problem to leave the fi- nal judgement to the users. Based on such an observation, this paper treats the re-identification problem as a ranking problem and directly optimizes a listwise ranking function named Mean Reciprocal Rank (MRR), which is considered by us to be able to generate results closest to human expec- tations. Using a maximum-margin based structured learn- ing model, we are able to show improved re-identification results on widely-used benchmark datasets.
A robust approach for on-line and off-line threat detection based on event tree similarity analysis
Abstract
The security of railway and mass-transit systems is increasingly dependant on the effectiveness of integrated Security Management Systems (SMS), which are meant to detect threats and to provide operators with information required for alarm verification purposes. In order to lower the false alarm rate and improve the detection reliability of threat scenarios, event correlation capabilities need to be integrated into the SMS. In this paper an existing approach based on a-priori defined event patterns is extended using a heuristic situation recognition approach which is more robust to both imperfect scenario modeling (human faults) and missed detections (sensor faults). The approach is based on similarity analysis between the event trees representing scenarios and it is effective both on-line and off-line. Applied on-line, it allows for an earlier and more fault-tolerant threat detection, since scenario matching is not required to be complete nor exact. Applied off-line, its effectiveness is twofold: first, it allows for detecting redundancies when updating the scenario repository; secondly, it enhances the post-event forensic search of suspicious behaviors not previously stored in the scenario repository. The strategy is being experimented in the context of railway protection.
On Building DecentralizedWide-Area Surveillance Networks based on ONVIF
Abstract
In this paper we present a decentralized surveillance network composed of IP video cameras, analysis devices and a central node which collects information and displays it in a 3D model of the complete area. The exchange of information between all components in the surveillance network takes place according to the ONVIF specification, therefore ensuring interoperability between products complying with the specification and flexibility regarding the integration of new devices and services. The collected information is displayed in a 3D model of the surveilled area, therefore providing a comfortable overview of the activity in large environments and offering the user an intuitive way to eventually interact with network devices.
Multi-Agent System for Moving Object Segmentation and Tracking
Abstract
Video segmentation and tracking have been important and challenging issues for many video processing. A novel spatiotemporal video object segmentation and tracking algorithm is proposed in this paper. This algorithm is based on multi-agent system and active contour technique. The multi-agent system is composed of a set of supervisor and explorator agents. The agents are communicating and inspired in their conduct from active contour technique, more precisely the "Level Sets". We used the DIMA platform to implement this algorithm. Experimental results indicate that the proposed algorithm is more robust than previous approaches.
- 12:00 — 13:30
- Lunch
- 13:30 — 17:30
- Workshop: Resource Aware Sensor and surveillance NETworkS (RAWSNETS)
Keynote: Mohan S. Kankanhalli - Design Issues in Multi-Camera Systems (more...)
-
Dynamic Resource Allocation for Probabilistic Tracking via Attentive Sensing and Sampling
Abstract
In the context of Ambient Intelligence a fundamental challenge is the design of monitoring technologies able to infer activities of people at-a-distance, employing nonintrusive sensors. Ideally, such solutions should operate in real time using minimal resources and scale to environments with complex topologies. These requirements naturally emerge in application domains such as Security & Surveillance, Ambient Assisted Living, Retail Monitoring, etc., and new research challenges are to be faced to push current state-of-the-art towards meeting them. In line with this trend, our recent efforts detailed in this paper focus on some of the limitations of traditional multi-camera based tracking methods arising in this context, which are characterized by passive sensing and limited adaptation.
Activity Aware Video Collection to Minimize Resource Usage in Smart Camera Nodes
Abstract
not available
Modeling and Optimization of Dynamic Signal Processing in Resource-Aware Sensor Networks
Abstract
Sensor node processing in resource-aware sensor networks is often critically dependent on dynamic signal processing functionality ? i.e., signal processing functionality in which computational structure must be dynamically assessed and adapted based on time-varying environmental conditions, operating constraints or application requirements. In dynamic signal processing systems, it is important to provide flexibility for run-time adaptation of application behavior and execution characteristics, but in the domain of resource-aware sensor networks, such flexibility cannot come with significant costs in terms of power consumption overhead or reduced predictability. In this paper, we review a variety of complementary models of computation that are being developed as part of the dataflow interchange format (DIF) project to facilitate efficient and reliable implementation of dynamic signal processing systems. We demonstrate these methods in the context of resource-aware sensor networks.
Dynamic Resource Aware Sensor Networks: Integration of Sensor Cloud and ERPs
Abstract
Today?s work in the sensor networks community focuses on collecting and processing data from specific networks with associated base stations. One of the most important requirements in these networks is minimizing resource usage such as processing power and storage size on sensor nodes. Resource constraints in the sensor nodes can be divided into four categories: energy, communication, storage and computational power. In this paper, we present an efficient deployment of sensors with possibility of accessing most recent data through information obtained from ERP (enterprise resource planning) systems? re-configuration models. In this scheme the probability of losing any precious data or events would be minimized. In other words, our main focus in this paper is using ERP or high level distributed decision making systems? processed data or prediction models to reduce resource usage needed on sensor nodes. In the area of integration of sensors and ERPs or distributed decision making systems, very little work has been reported. However, those reported in the literature, mostly address relaying data from sensors to ERPs and tend to largely ignore issues that come with sensor resource constraints. Also they don?t make use of the information processed and generated by ERPs in the cloud environment to optimize power consumption and transmission frequency of sensors, which is our aim.
- 13:30 — 17:30
- Workshop: Activity monitoring by multi-camera surveillance systems (AMMCSS)
-
Determining Operational Measures from Multi-Camera Surveillance Systems using Soft Biometrics
Abstract
CCTV and surveillance networks are increasingly being used for operational as well as security tasks. One emerging area of technology that lends itself to operational analytics is soft biometrics. Soft biometrics can be used to describe a person and detect them throughout a sparse multicamera network. This enables them to be used to perform tasks such as determining the time taken to get from point to point, and the paths taken through an environment by detecting and matching people across disjoint views. However, in a busy environment where there are 100?s if not 1000?s of people such as an airport, attempting to monitor everyone is highly unrealistic. In this paper we propose an average soft biometric, that can be used to identity people who look distinct, and are thus suitable for monitoring through a large, sparse camera network. We demonstrate how an average soft biometric can be used to identify unique people to calculate operational measures such as the time taken to travel from point to point.
Real Time Complex Event Detection for Resource-Limited Multimedia Sensor Networks
Abstract
This paper presents a real-time complex event detection concept for resource-limited multimedia sensor networks. A comprehensive solution based on Answer Set Programming (ASP) is developed. We show that ASP is an appropriate solution to detect a large number of simple and complex events (video-audio understanding) on platforms with limited resources e.g. power consumption, memory and processing power. We underline the major problems of the existing paradigms for complex event detection (based on e.g. logic programming and SemanticWeb), with a special focus on the major challenges which reduce the performance of real-time event detection. Finally, we demonstrate the high performance of ASP compared to that of Semantic Web.
A Game-theoretic Design for Collaborative Tracking in a Video Camera Network
Abstract
Tracking a moving target of interest at a high resolution with a dynamically designated network camera while ensuring complete-coverage of the area under surveillance of a video camera network can be a challenging task. Game theory can be applied to the situation, treating individual cameras as players and area coverage as utility. Camera collaboration is needed when one camera handoff the job of tracking a moving target of interest to another. By taking into account coverage shared by two cameras as secondary utility and giving precedence to border-covering camera directions, we can improve the performance of collaborative tracking, reducing complete-coverage failures as well as their associated uncovered blocks between handoffs. The cost of camera adjustment involved in a handoff can also be reduced this way.
Multi-Camera Detection Association for 3D Localisation
Abstract
A multi-camera system is described for 3-D localisation of subjects within a confined space. In particular, we present a novel neighbourhood association algorithm to solve the problem of associating detections in multiple camera views with subjects. To evaluate our approach, experiments were conducted using multiple view video sequences of up to four subjects simulating typical passenger behaviour on a bus. ROC curves were generated for three different versions which showed that for smaller values of the neighbourhood radius parameter, the system tended to over-estimate the number of subjects. However, increasing the radius reduced the over-estimation from 60% to 5%.
Improved Person Detection in Industrial Environments using Multiple Self-Calibrated Cameras
Abstract
Person detection is a challenging task in industrial environments which typically feature rapidly changing conditions of illumination and the presence of occluding objects and cluttered background. This paper proposes a series of algorithms for improving the robustness of person detection in such harsh industrial environments. Based on a state-ofthe- art person detector, significant robustness and automation is achieved by introducing automatic ground plane estimation, confidence filtering, cross-camera correspondence estimation and multi-camera fusion. Detailed experiments made on an industrial dataset that captures an automotive assembly process show the stepwise improvement when combining the above mentioned techniques in a fully unsupervised manner.
Multiple Views Based Human Motion Tracking in Surveillance Videos
Abstract
Most work on activity recognition focuses on 2D im- age properties, holistic spatiotemporal representations, or space-time shapes in image domain rather than with 3D pose in a body-centric or world frame. Such techniques rely on advanced pattern recognition algorithms and in- terpreting complex behavioral patterns. In this work we posit that it is possible to achieve 3D pose tracking using videos recorded in multi-camera surveillance systems. We show experimental results that were obtained on PETS 2009 datasets. The estimation of the 3D articulated motion is achieved using a modified particle swarm optimization.
HSV and RGB color histograms comparing for objects tracking among non overlapping FOVs, using CBTF
Abstract
Object tracking over wide-areas, such as an airport, the downtown of a large city or any large public area, is done by multiple cameras. Especially in realistic application, those cameras have non overlapping Field of Views (FOVs). Multiple camera tracking is very important to establish correspondence among detected objects across different cameras. In this paper we investigate color histogram techniques to evaluate inter-camera tracking algorithm based on object appearances. We compute HSV and RGB color histograms in order to evaluate their performance in establishing correspondence between object appearances in different FOVs before and after Cumulative Brightness Transfer Function (CBTF).
- 18:00 —
- Reception
Boat Trip at Lake Wörthersee
