Abstract: Automatic access control systems (ACS) based on the human biometrics or physical tokens are widely employed in public and private areas. Yet these systems, in their conventional forms, are restricted to active interaction from the users. In scenarios where users are not cooperating with the system, these systems are challenged. Failure in cooperation with the biometric systems might be intentional or because the users are incapable of handling the interaction procedure with the biometric system or simply forget to cooperate with it, due to for example, illness like dementia. This work introduces a challenging bimodal database, including face and hand information of the users when they approach a door to open it by its handle in a noncooperative context. We have defined two (an easy and a challenging) protocols on how to use the database. We have reported results on many baseline methods, including deep learning techniques as well as conventional methods on the database. The obtained results show the merit of the proposed database and the challenging nature of access control with non-cooperative users.

Abstract: In civilian video security monitoring, retrieving and tracking a person of interest often rely on witness testimony and their appearance description. Deployed systems rely on a large amount of annotated training data and are expected to show consistent performance in diverse areas and gen-eralize well between diverse settings w.r.t. different view-points, illumination, resolution, occlusions, and poses for indoor and outdoor scenes. However, for such generalization, the system would require a large amount of various an-notated data for training and evaluation. The WACV 2023 Pedestrian Attribute Recognition and Attributed-based Per-son Retrieval Challenge (UPAR-Challenge) aimed to spot-light the problem of domain gaps in a real-world surveil-lance context and highlight the challenges and limitations of existing methods. The UPAR dataset, composed of 40 important binary attributes over 12 attribute categories across four datasets, was extended with data captured from a low-flying UAV from the P-DESTRE dataset. To this aim, 0.6M additional annotations were manually labeled and vali-dated. Each track evaluated the robustness of the competing methods to domain shifts by training on limited data from a specific domain and evaluating using data from unseen do-mains. The challenge attracted 41 registered participants, but only one team managed to outperform the baseline on one track, emphasizing the task's difficulty. This work de-scribes the challenge design, the adopted dataset, obtained results, as well as future directions on the topic.

Abstract: Autonomous driving is a key factor for future mobility. Properly perceiving the environment of the vehicles is essential for a safe driving, which requires computing accurate geometric and semantic information in real-time. In this paper, we challenge state-of-the-art computer vision algorithms for building a perception system for autonomous driving. An inherent drawback in the computation of visual semantics is the trade-off between accuracy and computational cost. We propose to circumvent this problem by following an offline-online strategy. During the offline stage dense 3D semantic maps are created. In the online stage the current driving area is recognized in the maps via a re-localization process, which allows to retrieve the pre-computed accurate semantics and 3D geometry in realtime. Then, detecting the dynamic obstacles we obtain a rich understanding of the current scene. We evaluate quantitatively our proposal in the KITTI dataset and discuss the related open challenges for the computer vision community.

Abstract: Despite the outperformance of Support Vector Machine (SVM) on many practical classification problems, the algorithm is not directly applicable to multi-dimensional trajectories having different lengths. In this paper, a new class of SVM that is applicable to trajectory classification, such as action recognition, is developed by incorporating two efficient time-series distances measures into the kernel function.
Dynamic Time Warping and Longest Common Subsequence distance measures along with their derivatives are
employed as the SVM kernel. In addition, the pairwise proximity learning strategy is utilized in order to make use of non-positive semi-definite kernels in the SVM formulation. The proposed method is employed for a challenging classification problem: action recognition by depth cameras using only skeleton data; and evaluated on three benchmark action datasets. Experimental results demonstrate the outperformance of our methodology compared to the state-ofthe-art on the considered datasets.

Abstract: Handwritten Text Recognition (HTR) is still a challenging problem because it must deal with two important difficulties: the variability among writing styles, and the scarcity of labelled data. To alleviate such problems, synthetic data generation and data augmentation are typically used to train HTR systems. However, training with such data produces encouraging but still inaccurate transcriptions in real words. In this paper, we propose an unsupervised writer adaptation approach that is able to automatically adjust a generic handwritten word recognizer, fully trained with synthetic fonts, towards a new incoming writer. We have experimentally validated our proposal using five different datasets, covering several challenges (i) the document source: modern and historic samples, which may involve paper degradation problems; (ii) different handwriting styles: single and multiple writer collections; and (iii) language, which involves different character combinations. Across these challenging collections, we show that our system is able to maintain its performance, thus, it provides a practical and generic approach to deal with new document collections without requiring any expensive and tedious manual annotation step.