Interactive detection of incrementally learned concepts in images with ranking and semantic query interpretation

This research was performed in the GOOSE project, which is jointly funded by the MIST research program of the Dutch Ministry of Defense and the AMSN enabling technology program.The number of networked cameras is growing exponentially. Multiple applications in different domains result in an increasing need to search semantically over video sensor data. In this paper, we present the GOOSE demonstrator, which is a real-time general-purpose search engine that allows users to pose natural language queries to retrieve corresponding images. Top-down, this demonstrator interprets queries, which are presented as an intuitive graph to collect user feedback. Bottomup, the system automatically recognizes and localizes concepts in images and it can incrementally learn novel concepts. A smart ranking combines both and allows effective retrieval of relevant images.peer-reviewe

TNO at TRECVID 2013 : multimedia event detection and instance search

We describe the TNO system and the evaluation results for TRECVID 2013 Multimedia Event Detection (MED) and instance search (INS) tasks. The MED system consists of a bag-of-word (BOW) approach with spatial tiling that uses low-level static and dynamic visual features, an audio feature and high-level concepts. Automatic speech recognition (ASR) and optical character recognition (OCR) are not used in the system. In the MED case with 100 example training videos, support-vector machines (SVM) are trained and fused to detect an event in the test set. In the case with 0 example videos, positive and negative concepts are extracted as keywords from the textual event description and events are detected with the high-level concepts. The MED results show that the SIFT keypoint descriptor is the one which contributes best to the results, fusion of multiple low-level features helps to improve the performance, and the textual event-description chain currently performs poorly. The TNO INS system presents a baseline open-source approach using standard SIFT keypoint detection and exhaustive matching. In order to speed up search times for queries a basic map-reduce scheme is presented to be used on a multi-node cluster. Our INS results show above-median results with acceptable search times.This research for the MED submission was performed in the GOOSE project, which is jointly funded by the enabling technology program Adaptive Multi Sensor Networks (AMSN) and the MIST research program of the Dutch Ministry of Defense. The INS submission was partly supported by the MIME project of the creative industries knowledge and innovation network CLICKNL.peer-reviewe

Towards an Operational Sensor-Fusion System for Anti-Personnel Landmine Detection

To acquire detection performance required for an operational system for the detection of anti-personnel landmines, it is necessary to use multiple sensors and sensor-fusion techniques. This paper describes five decision-level sensor-fusion techniques and their common optimisation method. Th

A Comparison of Decision-Level Sensor-Fusion Methods for Anti-Personnel Landmine Detection

We present the sensor-fusion results obtained from measurements within the European research project ground explosive ordinance detection (GEODE) system that strives for the realisation of a vehicle-mounted, multi-sensor, anti-personnel landmine-detection system for humanitarian de-mining. The system has three sensor types: a metal detector (MD), an infrared camera (IR), and a ground penetrating radar (GPR). The output of the sensors is processed to produce confidence levels on a grid covering the test-bed. A confidence level expresses a confidence or belief in a landmine detection on a certain position. The grid with confidence levels is the input for the decision-level sensor-fusion and provides a co-registration of the sensors. The applied fusion methods are naive Bayes' approaches, Dempster Shafer theory, fuzzy probabilities, a rule-based method, and voting techniques. To compare fusion methods and to analyse the capacity of a method to separate landmines from the background on the basis of the output of different sensors, we provide an analysis of the different methods by viewing them as discriminant functions in the sensor confidence space. The results of experiments on real sensor data are evaluated with the leave-one-out method

Depth Fusion for Anti-Personnel Landmine Detection

In this paper we introduce the concept of depth fusion for anti-personnel landmine detection. Depth fusion is an extension of common sensor-fusion techniques for landmine detection. The difference lies within the fact that fusion of sensor data is performed in different physical depth layers. In order to do so, it requires a sensor that provides depth information for object detections. Our ground-penetrating radar (GPR) fulfills this requirement. Depth fusion is then taken as the combination of the output of sensor fusion of all layers. The underlying idea is that sensor fusion for the surface layer has a different weighing of the sensors when compared with the sensor fusion in the deep layers because of apparent sensor characteristics. For example, a thermal infrared (TIR) sensor hardly adds information to the sensor fusion in the deep layers. Furthermore, GPR has difficulties suppressing clutter in the surface layer. As such, the surface fusion should emphasize on the TIR sensor, whereas sensor fusion in the deep layers should have a higher weighing of the GPR. This a priori information can be made explicit by choosing for a depth-fusion approach. Experimental results from measurements at the TNO-FEL test facility are presented that validate our depth-fusion concepts

Feature-Based Detection of Landmines in Infrared Images

High detection performance is required for an operational system for the detection of landmines. Humanitarian de-mining scenarios, combined with inherent difficulties of detecting landmines on an operational (vibration, motion, atmosphere) as well as a scenario level (clutter, soil type, terrain), result in high levels of false alarms for most sensors. To distinguish a landmine from background clutter one or more discriminating object features have to be found. The researc