Search

An effective person re-identification (re-ID) model should learn feature representations that are both discriminative, for distinguishing similar-looking people, and generalisable, for deployment across datasets without any adaptation. In this paper, we develop novel CNN architectures to address both challenges. First, we present a re-ID CNN termed omni-scale network (OSNet) to learn features that not only capture different spatial scales but also encapsulate a synergistic combination of multiple scales, namely omni-scale features. The basic building block consists of multiple convolutional streams, each detecting features at a certain scale. For omni-scale feature learning, a unified aggregation gate is introduced to dynamically fuse multi-scale features with channel-wise weights. OSNet is lightweight as its building blocks comprise factorised convolutions. Second, to improve generalisable feature learning, we introduce instance normalisation (IN) layers into OSNet to cope with cross-dataset discrepancies. Further, to determine the optimal placements of these IN layers in the architecture, we formulate an efficient differentiable architecture search algorithm. Extensive experiments show that, in the conventional same-dataset setting, OSNet achieves state-of-the-art performance, despite being much smaller than existing re-ID models. In the more challenging yet practical cross-dataset setting, OSNet beats most recent unsupervised domain adaptation methods without using any target data. Our code and models are released at https://github.com/KaiyangZhou/deep-person-reid . [ABSTRACT FROM AUTHOR]

We present a novel multifeature video object trajectory clustering algorithm that estimates common patterns of behaviors and isolates outliers. The proposed algorithm is based on four main steps, namely the extraction of a set of representative trajectory features, non-parametric clustering, cluster merging and information fusion for the identification of normal and rare object motion patterns. First we transform the trajectories into a set of feature spaces on which mean-shift identifies the modes and the corresponding clusters. Furthermore, a merging procedure is devised to refine these results by combining similar adjacent clusters. The final common patterns are estimated by fusing the clustering results across all feature spaces. Clusters corresponding to reoccurring trajectories are considered as normal, whereas sparse trajectories are associated to abnormal and rare events. The performance of the proposed algorithm is evaluated on standard data-sets and compared with state-of-the-art techniques. Experimental results show that the proposed approach outperforms state-of-the-art algorithms both in terms of accuracy and robustness in discovering common patterns in video as well as in recognizing outliers. Index Terms--Anomaly detection, clustering, mean-shift, trajectory analysis.

We propose a filtering framework for multitarget tracking that is based on the probability hypothesis density (PHD) filter and data association using graph matching. This framework can be combined with any object detectors that generate positional and dimensional information of objects of interest. The PHD filter compensates for missing detections and removes noise and clutter. Moreover, this filter reduces the growth in complexity with the number of targets from exponential to linear by propagating the first-order moment of the multitarget posterior, instead of the full posterior. In order to account for the nature of the PHD propagation, we propose a novel particle resampling strategy and we adapt dynamic and observation models to cope with varying object scales. The proposed resampling strategy allows us to use the PHD filter when a priori knowledge of the scene is not available. Moreover, the dynamic and observation models are not limited to the PHD filter and can be applied to any Bayesian tracker that can handle state-dependent variances. Extensive experimental results on a large video surveillance dataset using a standard evaluation protocol show that the proposed filtering framework improves the accuracy of the tracker, especially in cluttered scenes. Index Terms--Monte Carlo, probability hypothesis density (PHD) filter, surveillance, tracking.

In this paper, we propose a tracking algorithm based on an adaptive multifeature statistical target model. The features are combined in a single particle filter by weighting their contributions using a novel reliability measure derived from the particle distribution in the state space. This measure estimates the reliability of the information by measuring the spatial uncertainty of features. A modified resampling strategy is also devised to account for the needs of the feature reliability estimation. We demonstrate the algorithm using color and orientation features. Color is described with partwise normalized histograms. Orientation is described with histograms of the gradient directions that represent the shape and the internal edges of a target. A feedback from the state estimation is used to align the orientation histograms as well as to adapt the scales of the filters to compute the gradient. Experimental results over a set of real-world sequences show that the proposed feature weighting procedure outperforms state-of-the-art solutions and that the proposed adaptive multifeature tracker improves the reliability of the target estimate while eliminating the need of manually selecting each feature's relevance. Index Terms--Color histogram, feature reliability, multifeature, orientation histogram, particle filter, representation, tracking.

We present an encoding framework which exploits semantics for video content delivery. The video content is organized based on the idea of main content message. In the work reported in this paper, the main content message is extracted from the video data through semantic video analysis, an application-dependent process that separates relevant information from non relevant information. We use here semantic analysis and the corresponding content annotation under a new perspective: the results of the analysis are exploited for object-based encoders, such as MPEG-4, as well as for frame-based encoders, such as MPEG-1. Moreover, the use of MPEG-7 content descriptors in conjunction with the video is used for improving content visualization for narrow channels and devices with limited capabilities. Finally, we analyze and evaluate the impact of semantic video analysis in video encoding and show that the use of semantic video analysis prior to encoding sensibly reduces the bandwidth requirements compared to traditional encoders not only for an object-based encoder but also for a frame-based encoder. Index Terms--Metadata, MPEG, object segmentation, video analysis, video encoding.

We present an algorithm for tracking video objects which is based on a hybrid strategy. This strategy uses both object and region information to solve the correspondence problem. Low-level descriptors are exploited to track object's regions and to cope with track management issues. Appearance and disappearance of objects, splitting and partial occlusions are resolved through interactions between regions and objects. Experimental results demonstrate that this approach has the ability to deal with multiple deformable objects, whose shape varies over time. Furthermore, it is very simple, because the tracking is based on the descriptors, which represent a very compact piece of information about regions, and they are easy to define and track automatically. Finally, this procedure implicitly provides one with a description of the objects and their track, thus enabling indexing and manipulation of the video content. Index Terms--Indexing, low level descriptors, object segmentation, object tracking.

The use of perovskite materials for thermochemical energy storage and oxygen separation has been gaining momentum in recent years due to their ability to topotactically exchange large volumes of oxygen, and their chemical and structural flexibility. B-site substituted SrCoO3-δ derivatives have previously been investigated as promising materials for intermediate temperature solid oxide fuel cell cathodes due to the stabilization of a 3 C perovskite structure with high electronic and ionic conductivity that allows large oxygen storage capabilities. Here, antimony-substituted strontium cobalt oxides are investigated and identified as new candidate materials for thermochemical oxygen separation applications. In this work we shed light on the exceptional redox kinetics and cyclability of antimony-substituted variants undergoing oxygen exchange at intermediate temperatures (500 to 800 °C). Through the use of density functional theory and isothermal gas atmosphere switching, we demonstrate how the inductive effect of the more electronegative antimony dopants in the Co position, facilitates the kinetics of metal oxide oxidation, whilst hindering reduction reactions. SrCo0.95Sb0.05O3−δ was identified to isothermally evolve 3.76 cm3 g−1 of oxygen at 500 °C and calculated to produce up to 10.44 cm3 g−1 under temperature-swing reaction configurations aligning with previously reported materials. [ABSTRACT FROM AUTHOR]