Unveiling unusual or hostile events by observing manifold moving persons in a crowd is a challenging task for human
operators, especially when sitting in front of monitor walls for hours. Typically, hostile events are rare. Thus, due to
tiredness and negligence the operator may miss important events. In such situations, an automatic alarming system is
able to support the human operator. The system incorporates a processing chain consisting of (1) people tracking, (2)
event detection, (3) data retrieval, and (4) display of relevant video sequence overlaid by highlighted regions of interest.
In this paper we focus on the event detection stage of the processing chain mentioned above. In our case, the selected
event of interest is the encounter of people. Although being based on a rather simple trajectory analysis, this kind of
event embodies great practical importance because it paves the way to answer the question "who meets whom, when and
where". This, in turn, forms the basis to detect potential situations where e.g. money, weapons, drugs etc. are handed
over from one person to another in crowded environments like railway stations, airports or busy streets and places etc..
The input to the trajectory analysis comes from a multi-object video-based tracking system developed at IOSB which is
able to track multiple individuals within a crowd in real-time . From this we calculate the inter-distances between all
persons on a frame-to-frame basis. We use a sequence of simple rules based on the individuals' kinematics to detect the
event mentioned above to output the frame number, the persons' IDs from the tracker and the pixel coordinates of the
meeting position. Using this information, a data retrieval system may extract the corresponding part of the recorded
video image sequence and finally allows for replaying the selected video clip with a highlighted region of interest to
attract the operator's attention for further visual inspection.
A manned platform is to be equipped with a Synthetic Aperture Radar (SAR) based Automatic Target Recognition
(ATR) system for precision targeting. The platform's airworthiness has to be approved including the ATR system, i.e. the
ATR system needs to be qualified appropriately.
Part of the airworthiness approval is a hazard analysis. In general, this is carried out to make sure that the probability of a
fatal error in one hour of flight is 10-9 or lower.
To date, error probabilities of a SAR-based ATR system, i.e. error probabilities of detection and classification, must be
assumed to lie above 10-9 per hour. This is one reason why existing rules of engagement demand "Man-in-the loop", i.e.
to display the result of the ATR system to the pilot.
Components to the ATR system are consequently
a Synthetic Aperture Radar (SAR) sensor
an Automatic Target Recognition (ATR) SAR image processing unit, and
a Human Machine Interface (HMI) to the pilot.
The aim of the work reported in this contribution was to identify those performance features of the thus defined ATR
system that are relevant to airworthiness approval, and to define the procedures to determine the feature values.
The paper contains the analysis of a reference case of an airworthiness-approved technical system with an error
probability above 10-9 per hour and a result display to the pilot. In the light of the analysis results, it concludes with an
outlook to the airworthiness approval of the ATR system.
Proc. SPIE. 7114, Electro-Optical Remote Sensing, Photonic Technologies, and Applications II
KEYWORDS: Unmanned aerial vehicles, Detection and tracking algorithms, Cameras, Sensors, Control systems, Data acquisition, Data processing, Error control coding, Information security, Situational awareness sensors
If for a given application, candidate tracking methods for humans need to be selected and optimized, then relevant sensor
and truth data as well as appropriate assessment criteria are required. In the work reported in this contribution we used
data recently collected in a riot control scenario. We then processed the sensor data using a set of tracking methods from
literature. Tracking results and truth data allowed us to deduce metrics that reflect the usefulness of a tracking method for
the selected scenario. The software implementation of the assessment criteria, together with sensor and truth data, forms
a benchmark for tracking algorithms in a riot control scenario. It can be used by developers to optimize their tracking
systems and to demonstrate their usefulness for application in a riot control scenario. The performance and robustness of
optimized tracking methods can considerably improve situational awareness in a riot control scenario.
Quick and precise response is essential for riot squads when coping with escalating violence in crowds. Often it is just a single person, known as the leader of the gang, who instigates other people and thus is responsible of excesses. Putting this single person out of action in most cases leads to a de-escalating situation. Fostering de-escalations is one of the main tasks of crowd and riot control. To do so, extensive situation awareness is mandatory for the squads and can be promoted by technical means such as video surveillance using sensor networks.
To develop software tools for situation awareness appropriate input data with well-known quality is needed. Furthermore, the developer must be able to measure algorithm performance and ongoing improvements. Last but not least, after algorithm development has finished and marketing aspects emerge, meeting of specifications must be proved.
This paper describes a multisensor benchmark which exactly serves this purpose. We first define the underlying algorithm task. Then we explain details about data acquisition and sensor setup and finally we give some insight into quality measures of multisensor data. Currently, the multisensor benchmark described in this paper is applied to the development of basic algorithms for situational awareness, e.g. tracking of individuals in a crowd.
This contribution describes the results of a collaboration the objective of which was to technically validate an assessment approach for automatic target recognition (ATR) components1. The approach is intended to become a standard for component specification and acceptance test during development and procurement and includes the provision of appropriate tools and data.
The collaboration was coordinated by the German Federal Office for Defense Technology and Procurement (BWB). Partners besides the BWB and the group Assessment of Fraunhofer IITB were ATR development groups of EADS Military Aircraft, EADS Dornier and Fraunhofer IITB.
The ATR development group of IITB contributed ATR results and developer's expertise to the collaboration while the industrial partners contributed ATR results and their expertise both from the developer's and the system integrator's point of view. The assessment group's responsibility was to provide task-relevant data and assessment tools, to carry out performance analyses and to document major milestones.
The result of the collaboration is twofold: the validation of the assessment approach by all partners, and two approved benchmarks for specific military target detection tasks in IR and SAR images. The tasks are defined by parameters including sensor, viewing geometries, targets, background etc. The benchmarks contain IR and SAR sensor data, respectively. Truth data and assessment tools are available for performance measurement and analysis. The datasets are split into training data for ATR optimization and test data exclusively used for performance analyses during acceptance tests. Training data and assessment tools are available for ATR developers upon request.
The work reported in this contribution was supported by the German Federal Office for Defense Technology and Procurement (BWB), EADS Dornier, and EADS Military Aircraft.
This contribution presents a comprehensive framework for algorithm evaluation. When we speak of evaluation, we have in mind that first the performance of an algorithm is measured and then the measured performance is assessed with regard to a given application. The performance assessment is done by applying an assessment function that uses desired values for the performance measures and weighting factors giving the importance of each measure, thus considering the application- specific requirements. The algorithm evaluation's goal is to verify the specification of an algorithm. This specification is mainly given by the definition of the input data and the expected output data, both of which are determined by the application. Prior to the evaluation process the algorithm specification has to be laid down by analyzing the application in order to deduce its requirements as well as by defining the application relevant data sets. To organize this sequence of preparatory steps and to formalize the accomplishment of the evaluation we have developed a 3-phase approach, consisting of the definition phase, the tuning phase, and the evaluation phase. An extensive software toolbox has been developed to support the evaluation process.