Recent progress in multiple object tracking (MOT) has shown that a robust similarity score is key to the success of trackers. A good similarity score is expected to reflect multiple cues, e.g. appearance, location, and topology, over a long period of time. However, these cues are heterogeneous, making them hard to be combined in a unified network. As a result, existing methods usually encode them in separate networks or require a complex training approach. In this paper, we present a unified framework for similarity measurement which could simultaneously encode various cues and perform reasoning across both spatial and temporal domains. We also study the feature representation of a tracklet-object pair in depth, showing a proper design of the pair features can well empower the trackers. The resulting approach is named spatial-temporal relation networks (STRN). It runs in a feed-forward way and can be trained in an end-to-end manner. The state-of-the-art accuracy was achieved on all of the MOT15-17 benchmarks using public detection and online settings.
The evolution of appearance feature extractor is from hand-craft @cite_40 @cite_63 @cite_44 @cite_66 to deep networks @cite_49 @cite_54 @cite_58 @cite_27 @cite_71 . In this paper, we also utilize deep networks as our base appearance feature extractor. One crucial difference between the previous approaches lies in the way to build similarity from appearances. We utilize a hybrid of feature concatenation, cosine distance, location motion priors to compute the final similarities.
@cite_54 @cite_44 @cite_40 @cite_27 @cite_49 @cite_63 @cite_71 @cite_58 @cite_66
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A key issue regarding feature extraction is the capability of a technique to extract distinctive features to represent facial expressions while requiring a low computational complexity. In this study, the authors propose a novel approach for appearance-based facial feature extraction to perform the task of facial expression recognition on video sequences. The proposed spatiotemporal texture map (STTM) is capable of capturing subtle spatial and temporal variations of facial expressions with low computational complexity. First, face is detected using Viola–Jones face detector and frames are cropped to remove unnecessary background. Facial features are then modelled with the proposed STTM, which uses the spatiotemporal information extracted from three-dimensional Harris corner function. A block-based method is adopted to extract the dynamic features and represent the features in the form of histograms. The features are then classified into classes of emotion by the support vector machine classifier. The experimental results demonstrate that the proposed approach shows superior performance compared with the state-of-the-art approaches with an average recognition rate of 95.37, 98.56, and 84.52 on datasets containing posed expressions, spontaneous micro-expressions, and close-to-real-world expressions, respectively. They also show that the proposed algorithm requires low computational cost. We investigate the use of Deep Neural Networks for the classification of image datasets where texture features are important for generating class-conditional discriminative representations. To this end, we first derive the size of the feature space for some standard textural features extracted from the input dataset and then use the theory of Vapnik-Chervonenkis dimension to show that hand-crafted feature extraction creates low-dimensional representations which help in reducing the overall excess error rate. As a corollary to this analysis, we derive for the first time upper bounds on the VC dimension of Convolutional Neural Network as well as Dropout and Dropconnect networks and the relation between excess error rate of Dropout and Dropconnect networks. The concept of intrinsic dimension is used to validate the intuition that texture-based datasets are inherently higher dimensional as compared to handwritten digits or other object recognition datasets and hence more difficult to be shattered by neural networks. We then derive the mean distance from the centroid to the nearest and farthest sampling points in an n-dimensional manifold and show that the Relative Contrast of the sample data vanishes as dimensionality of the underlying vector space tends to infinity. Extracting local features from 3D shapes is an important and challenging task that usually requires carefully designed 3D shape descriptors. However, these descriptors are hand-crafted and require intensive human intervention with prior knowledge. To tackle this issue, we propose a novel deep learning model, namely circle convolutional restricted Boltzmann machine (CCRBM), for unsupervised 3D local feature learning. CCRBM is specially designed to learn from raw 3D representations. It effectively overcomes obstacles such as irregular vertex topology, orientation ambiguity on the 3D surface, and rigid or slightly non-rigid transformation invariance in the hierarchical learning of 3D data that cannot be resolved by the existing deep learning models. Specifically, by introducing the novel circle convolution, CCRBM holds a novel ring-like multi-layer structure to learn 3D local features in a structure preserving manner. Circle convolution convolves across 3D local regions via rotating a novel circular sector convolution window in a consistent circular direction. In the process of circle convolution, extra points are sampled in each 3D local region and projected onto the tangent plane of the center of the region. In this way, the projection distances in each sector window are employed to constitute a novel local raw 3D representation called projection distance distribution (PDD). In addition, to eliminate the initial location ambiguity of a sector window, the Fourier transform modulus is used to transform the PDD into the Fourier domain, which is then conveyed to CCRBM. Experiments using the learned local features are conducted on three aspects: global shape retrieval, partial shape retrieval, and shape correspondence. The experimental results show that the learned local features outperform other state-of-the-art 3D shape descriptors. In this paper, we present a system for sketch classification and similarity search. We used deep convolution neural networks (ConvNets), state of the art in the field of image recognition. They enable both classification and medium highlevel features extraction. We make use of ConvNets features as a basis for similarity search using k-Nearest Neighbors (kNN). Evaluation are performed on the TU-Berlin benchmark. Our main contributions are threefold: first, we use ConvNets in contrast to most previous approaches based essentially on hand crafted features. Secondly, we propose a ConvNet that is both more accurate and lighter faster than the two only previous attempts at making use of ConvNets for handsketch recognition. We reached an accuracy of 75.42 . Third, we shown that similarly to their application on natural images, ConvNets allow the extraction of medium-level and high-level features (depending on the depth) which can be used for similarity search.1