Real-time monitoring of crowded regions has crucial importance to avoid overload of people in certain areas. Understanding dynamics of large people crowds can also help to estimate future status of public areas. In order to bring an automatic solution to the problem, herein we introduce four different approaches based on keypoint extraction from airborne images. Using four different keypoint extraction methods separately, we form four different probability density functions (pdf) which hold information about density of people. With our experimental results, we discuss the strengths and weaknesses of these methods in detail. Besides using four different keypoint extraction methods, we also introduce fusion approaches in order to increase the robustness of the algorithm. Our promising experimental results indicate possible usage of the algorithm on real-time on board applications.

MAHNOB-HCI is a multimodal database recorded in response to affective stimuli with the goal of emotion recognition and implicit tagging research. A multimodal setup was arranged for synchronized recording of face videos, audio signals, eye gaze data, and peripheral/central nervous system physiological signals. Twenty-seven participants from both genders and different cultural backgrounds participated in two experiments. In the first experiment, they watched 20 emotional videos and self-reported their felt emotions using arousal, valence, dominance, and predictability as well as emotional keywords. In the second experiment, short videos and images were shown once without any tag and then with correct or incorrect tags. Agreement or disagreement with the displayed tags was assessed by the participants. The recorded videos and bodily responses were segmented and stored in a database. The database is made available to the academic community via a web-based system. The collected data were analyzed and single modality and modality fusion results for both emotion recognition and implicit tagging experiments are reported. These results show the potential uses of the recorded modalities and the significance of the emotion elicitation protocol.

Applications such as surveillance and human motion capture require high-bandwidth recording from multiple cameras. Furthermore, the recent increase in research on sensor fusion has raised the demand on synchronization accuracy between video, audio and other sensor modalities. Previously, capturing synchronized, high resolution video from multiple cameras required complex, inflexible and expensive solutions. Our experiments show that a single PC, built from contemporary low-cost computer hardware, could currently handle up to 470MB/s of input data. This allows capturing from 18 cameras of 780x580pixels at 60fps each, or 36 cameras at 30fps. Furthermore, we achieve accurate synchronization between audio, video and additional sensors, by recording audio together with sensor trigger- or timestamp signals, using a multi-channel audio input. In this way, each sensor modality can be captured with separate software and hardware, allowing maximal flexibility with minimal cost.

In this study we compare human and machine acceptability judgments for extreme variations in sign productions. We gathered acceptability judgments of 26 signers and scores of three different automatic gesture recognition (AGR) algorithms that could potentially be used for automatic acceptability judgments, in which case the correlation between human ratings and AGR scores may serve as an dasiaacceptability performancepsila measure. We found high human-human correlations, high AGR-AGR correlations, but low human-AGR correlations. Furthermore, in a comparison between acceptability and classification performance of the different AGR methods, classification performance was found to be an unreliable predictor of acceptability performance.

We have developed a prototype for a learning environment for deaf and hard of hearing children. This demonstration consists of hands-on experience with the prototype. In total, there are three exercises: 1) an introduction of all pictures and corresponding signs, 2) multiple choice sign-to-picture and 3) performing the sign that corresponds to the picture shown on the screen. The live recognition from a wide-angle stereo camera provides immediate feedback for the third exercise where the sign must be performed.

We present a method to automatically construct a sign language classifier for a previously unseen sign. The only required input of a new sign is one example, performed by a sign language tutor. The method works by comparing the measurements of the new sign to signs that have been trained on a large number of persons. The parameters of the respective trained classifier models are used to construct a classification model for the new sign. We show that the performance of a classifier constructed from an instructed sign is significantly better than that of dynamic time warping (DTW) with the same sign. Using only a single example, the proposed method has a performance comparable to a regular training with five examples, while being more stable because of the larger source of information.

To recognize speech, handwriting or sign language, many hybrid approaches have been proposed that combine Dynamic Time Warping (DTW) or Hidden Markov Models (HMM) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated because of conflicting likelihood modelling demands. To overcome these restrictions, we propose to use Statistical DTW (SDTW) only for time warping, while classifying the warped features with a different method. Two novel statistical classifiers are proposed (CDFD and Q-DFFM), both using a selection of discriminative features (DF), and are shown to outperform HMM and SDTW. However, we have found that combining likelihoods of multiple models in a second classification stage degrades performance of the proposed classifiers, while improving performance with HMM and SDTW. A proof-of-concept experiment, combining DFFM mappings of multiple SDTW models with SDTW likelihoods, shows that also for model-combining, hybrid classification can provide significant improvement over SDTW. Although recognition is mainly based on 3D hand motion features, these results can be expected to generalize to recognition with more detailed measurements such as hand/body pose and facial expression.

A 3D visual hand gesture recognition method is proposed that detects correctly performed signs from stereo camera input. Hand tracking is based on skin detection with an adaptive chrominance model to get high accuracy. Informative high level motion properties are extracted to simplify the classification task. Each example is mapped onto a fixed reference sign by Dynamic Time Warping, to get precise time correspondences. The classification is done by combining weak classifiers based on robust statistics. Each base classifier assumes a uniform distribution of a single feature, determined by winsorization on the noisy training set. The operating point of the classifier is determined by stretching the uniform distributions of the base classifiers instead of changing the threshold on the total posterior likelihood. In a cross validation with 120 signs performed by 70 different persons, 95% of the test signs were correctly detected at a false positive rate of 5%.

Usually, object detection is performed directly on (normalized) gray values or gray primitives like gradients or Haar-like features. In that case the learning of relationships between gray primitives, that describe the structure of the object, is the complete responsibility of the classifier. We propose to apply more knowledge about the image structure in the preprocessing step, by computing local isophote directions and curvatures, in order to supply the classifier with much more informative image structure features. However, a periodic feature space, like orientation, is unsuited for common classification methods. Therefore, we split orientation into two more suitable components. Experiments show that the isophote features result in better detection performance than intensities, gradients or Haar-like features.