We proposed a variational Bayesian model for time varying number of person tracking.Our tracker exploit cluttered visual observations obtained from person detectors.People entering and leaving scene are handled with birth and visibility processes.Competitive results are achieved over on classical tracking benchmarks datasets. Display Omitted Object tracking is an ubiquitous problem that appears in many applications such as remote sensing, audio processing, computer vision, human-machine interfaces, human-robot interaction, etc. Although thoroughly investigated in computer vision, tracking a time-varying number of persons remains a challenging open problem. In this paper, we propose an on-line variational Bayesian model for multi-person tracking from cluttered visual observations provided by person detectors. The paper has the following contributions. We propose a variational Bayesian framework for tracking an unknown and varying number of persons. Our model results in a variational expectation-maximization (VEM) algorithm with closed-form expressions both for the posterior distributions of the latent variables and for the estimation of the model parameters. The proposed model exploits observations from multiple detectors, and it is therefore multimodal by nature. Finally, we propose to embed both object-birth and object-visibility processes in an effort to robustly handle temporal appearances and disappearances. Evaluated on classical multiple person tracking datasets, our method shows competitive results with respect to state-of-the-art multiple-object tracking algorithms, such as the probability hypothesis density (PHD) filter, among others.
Methods for single object tracking consider only one object and usually involve an initialization step, a state update step, and a reinitialization step allowing to recover from failures. Practical initialization steps are based on generic object detectors allowing to scan the input image in order to find the object of interest @cite_15 @cite_39 . Object detectors can be used for the reinitialization step as well. However, using generic object detectors is problematic when other objects of the same kind than the tracked object are present in the visual scene. In order to resolve such ambiguities, different complementary appearance models have been proposed, such as object templates, color appearance models, edges (image gradients) and texture, (e.g. Gabor features and histogram of gradient orientations). Regarding the update step, the current state can be estimated from previous states and observations with either deterministic @cite_7 or probabilistic @cite_27 methods.
@cite_27 @cite_15 @cite_7 @cite_39
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This paper presents an adaptive tracking algorithm by learning hybrid object templates online in video. The templates consist of multiple types of features, each of which describes one specific appearance structure, such as flatness, texture, or edge corner. Our proposed solution consists of three aspects. First, in order to make the features of different types comparable with each other, a unified statistical measure is defined to select the most informative features to construct the hybrid template. Second, we propose a simple yet powerful generative model for representing objects. This model is characterized by its simplicity since it could be efficiently learnt from the currently observed frames. Last, we present an iterative procedure to learn the object template from the currently observed frames, and to locate every feature of the object template within the observed frames. The former step is referred to as feature pursuit, and the latter step is referred to as feature alignment, both of which are performed over a batch of observations. We fuse the results of feature alignment to locate objects within frames. The proposed solution to object tracking is in essence robust against various challenges, including background clutters, low-resolution, scale changes, and severe occlusions. Extensive experiments are conducted over several publicly available databases and the results with comparisons show that our tracking algorithm clearly outperforms the state-of-the-art methods. The main stated contribution of the Deformable Parts Model (DPM) detector of (over the Histogram-of-Oriented-Gradients approach of Dalal and Triggs) is the use of deformable parts. A secondary contribution is the latent discriminative learning. Tertiary is the use of multiple components. A common belief in the vision community (including ours, before this study) is that their ordering of contributions reflects the performance of detector in practice. However, what we have experimentally found is that the ordering of importance might actually be the reverse. First, we show that by increasing the number of components, and switching the initialization step from their aspect-ratio, left-right flipping heuristics to appearance-based clustering, considerable improvement in performance is obtained. But more intriguingly, we show that with these new components, the part deformations can now be completely switched off, yet obtaining results that are almost on par with the original DPM detector. Finally, we also show initial results for using multiple components on a different problem -- scene classification, suggesting that this idea might have wider applications in addition to object detection. In this paper, we propose a two-step method to recognize multiple-food images by detecting candidate regions with several methods and classifying them with various kinds of features. In the first step, we detect several candidate regions by fusing outputs of several region detectors including Felzenszwalb's deformable part model (DPM) [1], a circle detector and the JSEG region segmentation. In the second step, we apply a feature-fusion-based food recognition method for bounding boxes of the candidate regions with various kinds of visual features including bag-of-features of SIFT and CSIFT with spatial pyramid (SP-BoF), histogram of oriented gradient (HoG), and Gabor texture features. In the experiments, we estimated ten food candidates for multiple-food images in the descending order of the confidence scores. As results, we have achieved the 55.8 classification rate, which improved the baseline result in case of using only DPM by 14.3 points, for a multiple-food image data set. This demonstrates that the proposed two-step method is effective for recognition of multiple-food images. Camera networks are being used in more applications as different types of sensor networks are used to instrument large spaces. Here we show a method for localizing the cameras in a camera network to recover the orientation and position up to scale of each camera, even when cameras are wide-baseline or have different photometric properties. Using moving objects in the scene, we use an intra-camera step and an inter-camera step in order to localize. The intra-camera step compares frames from a single camera to build the tracks of the objects in the image plane of the camera. The inter-camera step uses these object image tracks from each camera as features for correspondence between cameras. We demonstrate this idea on both simulated and real data.