Pyramidal Fisher Motion for Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent `AVA Multiview Gait' dataset. The results show that this new approach achieves promising results in the problem of gait recognition.Comment: Submitted to International Conference on Pattern Recognition, ICPR, 201

Fisher Motion Descriptor for Multiview Gait Recognition

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to de ne custom spatial con gurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [1]) extracted on the di erent spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [2]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on `CASIA' dataset [3] (parts B and C), `TUM GAID' dataset [4], `CMU MoBo' dataset [5] and the recent `AVA Multiview Gait' dataset [6]. The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing di erent clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths

Long Range Automated Persistent Surveillance

This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

Izbira značilnic za detekcijo objektov dane vizualne kategorije na slikah

This master's thesis deals with the problem of selecting features for automatic detection of objects of a given visual category in images. The goal of object detection is to determine the locations and sizes of all objects of the given category in each input image, where the number of objects displayed in individual images is not known in advance. Various circumstances may contribute to the difficulty of object detection, such as cluttered backgrounds, diversity in the appearance of objects in the category, diversity in the scale of the displayed objects, and partial occlusions. The thesis primarily focuses on systems that represent each input image by a set of features and use the resulting image representations to form detection hypotheses. The feature set is assumed to be prepared by a two-step procedure in the training stage. In the first step, the initial feature set is extracted from a set of training images. In the second step, the features to be used in the test stage are selected from the initial set. The thesis is devoted to the second step, i.e., feature selection. An effective selection method reduces the computational complexity in the test stage and eliminates features that are useless or even harmful for detecting objects of a given category. In the master’s thesis, we present two feature selection methods. At the core of the first one (the so-called filter method) is a feature evaluation function that is based on a transformation of the problem of detecting objects in training images into a problem of classifying training images themselves. Since much existing theory and practice of feature selection pertains to the classification rather than detection domain, such a problem transformation greatly expands the range of applicable selection approaches. Because of its design, the feature evaluation function can be straightforwardly incorporated into the AdaBoost selection framework, which selects features by considering their interdependence in an implicit manner. The second selection method that is presented in this thesis (the so-called wrapper method) systematically runs the detection procedure on a fixed training image set for various candidate feature sets. The goal of such a selection approach is to find a feature set that enables the detection procedure to attain the highest detection accuracy on the training image set. The problem of finding the optimal candidate feature set can be easily transformed into a problem of finding the optimal vertex in the corresponding state space graph. In the implementation of the system, each of the two selection methods was integrated with a well-known detection approach and with two promising state-of-the-art feature extraction methods. We experimentally tested all four combinations of feature extraction and feature selection methods. The experiments were evaluated using five test image sets, each of which defined a visual category and an independent detection setup. In some cases, as it turned out, a very small number of selected features suffices to bring about a fairly high detection accuracy. Particularly remarkable results were achieved for the combination of the filter selection method, the feature extraction method due to Fidler et al. (2006), and the UIUC car dataset. In this setup, the selection of merely four features out of 952 initial candidates led to a detection accuracy that is comparable to the state-of-the-art results for the UIUC dataset