Multi Branch Siamese Network For Person Re-Identification

To capture robust person features, learning discriminative, style and view invariant descriptors is a key challenge in person Re-Identification (re-id). Most deep Re-ID models learn single scale feature representation which are unable to grasp compact and style invariant representations. In this paper, we present a multi branch Siamese Deep Neural Network with multiple classifiers to overcome the above issues. The multi-branch learning of the network creates a stronger descriptor with fine-grained information from global features of a person. Camera to camera image translation is performed with generative adversarial network to generate diverse data and add style invariance in learned features. Experimental results on benchmark datasets demonstrate that the proposed method performs better than other state of the arts methods

Tracking-by-Trackers with a Distilled and Reinforced Model

Visual object tracking was generally tackled by reasoning independently on fast processing algorithms, accurate online adaptation methods, and fusion of trackers. In this paper, we unify such goals by proposing a novel tracking methodology that takes advantage of other visual trackers, offline and online. A compact student model is trained via the marriage of knowledge distillation and reinforcement learning. The first allows to transfer and compress tracking knowledge of other trackers. The second enables the learning of evaluation measures which are then exploited online. After learning, the student can be ultimately used to build (i) a very fast single-shot tracker, (ii) a tracker with a simple and effective online adaptation mechanism, (iii) a tracker that performs fusion of other trackers. Extensive validation shows that the proposed algorithms compete with real-time state-of-the-art trackers

Improving MRI-based Knee Disorder Diagnosis with Pyramidal Feature Details

This paper presents MRPyrNet, a new convolutional neural network (CNN) architecture that improves the capabilities of CNN-based pipelines for knee injury detection via magnetic resonance imaging (MRI). Existing works showed that anomalies are localized in small-sized knee regions that appear in particular areas of MRI scans. Based on such facts, MRPyrNet exploits a Feature Pyramid Network to enhance small appearing features and Pyramidal Detail Pooling to capture such relevant information in a robust way. Experimental results on two publicly available datasets demonstrate that MRPyrNet improves the ACL tear and meniscal tear diagnosis capabilities of two state-of-the-art methodologies. Code is available at https://git.io/JtMPH

A supervised extreme learning committee for food recognition

Food recognition is an emerging topic in computer vision. The problem is being addressed especially in health-oriented systems where it is used as a support for food diary applications. The goal is to improve current food diaries, where the users have to manually insert their daily food intake, with an automatic recognition of the food type, quantity and consequent calories intake estimation. In addition to the classical recognition challenges, the food recognition problem is characterized by the absence of a rigid structure of the food and by large intra-class variations. To tackle such challenges, a food recognition system based on a committee classification is proposed. The aim is to provide a system capable of automatically choosing the optimal features for food recognition out of the existing plethora of available ones (e.g., color, texture, etc.). Following this idea, each committee member, i.e., an Extreme Learning Machine, is trained to specialize on a single feature type. Then, a Structural Support Vector Machine is exploited to produce the final ranking of possible matches by filtering out the irrelevant features and thus merging only the relevant ones. Experimental results show that the proposed system outperforms state-of-the-art works on four publicly available benchmark datasets. \ua9 2016 Elsevier Inc. All rights reserved

Visualizing Skiers' Trajectories in Monocular Videos

Trajectories are fundamental to winning in alpine skiing. Tools enabling the analysis of such curves can enhance the training activity and enrich broadcasting content. In this paper, we propose SkiTraVis, an algorithm to visualize the sequence of points traversed by a skier during its performance. SkiTraVis works on monocular videos and constitutes a pipeline of a visual tracker to model the skier's motion and of a frame correspondence module to estimate the camera's motion. The separation of the two motions enables the visualization of the trajectory according to the moving camera's perspective. We performed experiments on videos of real-world professional competitions to quantify the visualization error, the computational efficiency, as well as the applicability. Overall, the results achieved demonstrate the potential of our solution for broadcasting media enhancement and coach assistance.Comment: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), CVsports worksho

A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation settings, i.e. usually a bicubic downscaling of low-resolution (LR) image. However, in real-world settings, the LR degradation process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR, or real LR. Therefore, most SR methods are ineffective and inefficient in handling more than one degradation settings within a single network. To handle the multiple degradation, i.e. refers to multi-domain image super-resolution, we propose a deep Super-Resolution Residual StarGAN (SR2*GAN), a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. The proposed scheme is trained in a StarGAN like network topology with a single generator and discriminator networks. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.Comment: 5 pages, 6th International Conference on Smart and Sustainable Technologies 2021. arXiv admin note: text overlap with arXiv:2009.03693, arXiv:2005.0095