We present a novel approach for spatiotemporal saliency detection by optimizing a unified criterion of color contrast, motion contrast, appearance, and background cues. To this end, we first abstract the video by temporal superpixels. Second, we propose a novel graph structure exploiting the saliency cues to assign the edge weights. The salient segments are then extracted by applying a spectral foreground detection method, quantum cuts, on this graph. We evaluate our approach on several public datasets for video saliency and activity localization to demonstrate the favorable performance of the proposed video quantum cuts compared to the state of the art.

We present a novel video saliency detection method to support human activity recognition and weakly supervised training of activity detection algorithms. Recent research has emphasized the need for analyzing salient information in videos to minimize dataset bias or to supervise weakly labeled training of activity detectors. In contrast to previous methods we do not rely on training information given by either eye-gaze or annotation data, but propose a fully unsupervised algorithm to find salient regions within videos. In general, we enforce the Gestalt principle of figure-ground segregation for both appearance and motion cues. We introduce an encoding approach that allows for efficient computation of saliency by approximating joint feature distributions. We evaluate our approach on several datasets, including challenging scenarios with cluttered background and camera motion, as well as salient object detection in images. Overall, we demonstrate favorable performance compared to state-of-the-art methods in estimating both ground-truth eye-gaze and activity annotations.

In this paper, we address the problem of model-free online object tracking based on color representations. According to the findings of recent benchmark evaluations, such trackers often tend to drift towards regions which exhibit a similar appearance compared to the object of interest. To overcome this limitation, we propose an efficient discriminative object model which allows us to identify potentially distracting regions in advance. Furthermore, we exploit this knowledge to adapt the object representation beforehand so that distractors are suppressed and the risk of drifting is significantly reduced. We evaluate our approach on recent online tracking benchmark datasets demonstrating state-of-the-art results. In particular, our approach performs favorably both in terms of accuracy and robustness compared to recent tracking algorithms. Moreover, the proposed approach allows for an efficient implementation to enable online object tracking in real-time.

Robust multi-object tracking-by-detection requires the correct assignment of noisy detection results to object trajectories. We address this problem by proposing an online approach based on the observation that object detectors primarily fail if objects are significantly occluded. In contrast to most existing work, we only rely on geometric information to efficiently overcome detection failures. In particular, we exploit the spatio-temporal evolution of occlusion regions, detector reliability, and target motion prediction to robustly handle missed detections. In combination with a conservative association scheme for visible objects, this allows for real-time tracking of multiple objects from a single static camera, even in complex scenarios. Our evaluations on publicly available multi-object tracking benchmark datasets demonstrate favorable performance compared to the state-of-the-art in online and offline multi-object tracking.

Combining foreground images from multiple views by projecting them onto a common ground-plane has been recently applied within many multi-object tracking approaches. These planar projections introduce severe artifacts and constrain most approaches to objects moving on a common 2D ground-plane. To overcome these limitations, we introduce the concept of an occupancy volume - exploiting the full geometry and the objects' center of mass - and develop an efficient algorithm for 3D object tracking. Individual objects are tracked using the local mass density scores within a particle filter based approach, constrained by a Voronoi partitioning between nearby trackers. Our method benefits from the geometric knowledge given by the occupancy volume to robustly extract features and train classifiers on-demand, when volumetric information becomes unreliable. We evaluate our approach on several challenging real-world scenarios including the public APIDIS dataset. Experimental evaluations demonstrate significant improvements compared to state-of-the-art methods, while achieving real-time performance.

This paper proposes an efficient approach for semantic image classification by integrating additional contextual constraints such as class co-occurrences into a randomized forest classification framework. The randomized forest classifier performs an initial yet local classification on the pixel level by using powerful covariance matrix based descriptors as feature representation. Furthermore, we exploit multiple unsupervised image partitions to provide a reliable spatial region support and to capture the real object boundaries. An information theoretic driven approach detects consistently classified regions and generates a representative segmentation incorporating the classification result on the pixel level. Moreover, we use a conditional random field formulation to obtain a final labeling including context information individually generated for each test image. To illustrate state-of-the-art performance, we run experiments on the two versions of the MSRC [21] dataset with 9 and 21 object classes and on the PASCAL VOC2007 [5] image collection.

This paper describes a combination of an automated image acquisition method and a probabilistic tracking method for analysis of the 3D microstructure of a sheet of paper. A prototype which combines microtomy and light microscopy enables efficient and fully automated digitization of paper samples in high resolution. A particle filter based tracking method then allows to segment individual fibers from the obtained 3D data sets. The capability of accessing the properties of individual fibers enables analysis of e. g. 3D fiber mass distribution, 3D fiber orientation or fiber morphology. Experiments show that the method provides results consistent with the knowledge of paper experts.

This paper introduces a hierarchical approach for multi-component tracking, where the object-to-be-tracked is modeled as a group of spatial related parts. We propose to use a robust particle filtering framework for tracking the individual components and outline how the spatial coherency between the parts can be efficiently integrated by analyzing a two-level hierarchy of particle filters. Including spatial information allows to handle common tracking problems like occlusions, clutter or blur. Furthermore, the dynamic calculation of particle set uncertainties allows a dynamic adaption of stiffness values for the spatial model to e. g. force occluded parts to stay in spatial relation. The experimental section proves the robustness of the proposed tracker on challenging sequences of the VIVID-PETS database.