Estimating biometric and anthropometric attributes from gait sequences presents a promising alternative to traditional body measurement techniques, particularly in unconstrained or low-resource environments. However, inferring metric attributes from 2D silhouette-based gait representations remains challenging due to the lack of volumetric cues. In this work, we propose a novel training strategy that leverages the geometric priors encoded by PIFuHD—a high-resolution implicit function-based model for 3D human reconstruction—to inject structural supervision into a gait encoder. Our method introduces a feature reconstruction branch that distills volumetric knowledge from precomputed PIFuHD embeddings during training, enabling accurate prediction of anthropometric attributes at inference time from 2D silhouette sequences alone. We evaluate our approach on the Health&Gait dataset, achieving substantial improvements over baselines in predicting multiple attributes, including height, weight, BMI, and body circumferences. These results demonstrate that shape-aligned supervision from implicit 3D models can effectively bridge the gap between geometric reasoning and efficient biometric estimation from visual gait data.

Gait recognition has gained attention for its ability to identify individuals from afar. Current state-of-the-art approaches predominantly utilize visual information, such as silhouettes, or a combination of visual data and basic body pose information, including skeleton joint coordinates. However, the role of human pose in gait recognition is still underexplored, often leading to poorer results compared to visual approaches. In this work, we propose a novel hierarchical limb-based representation that enhances the depiction of body pose and can be applied to various pose descriptors. Our representation consists of three hierarchical levels: full body, body limbs (arms and legs), and middle limbs (forearms, lower arms, thighs, and shins). This structure enriches the gait description of the overall pose by incorporating the specific movements of each limb. Particularly, we investigate the application of our hierarchical arrangement using two different rich pose descriptors: heatmaps derived from 2D body skeletons and a dense representation obtained from pixel-wise estimation of body pose ( i.e DensePose). Furthermore, we introduce the PoseGaitGL family of models to better leverage the features derived from our pose representations. By employing our hierarchical pose representations, the proposed model achieves state-of-the-art results in pose-based gait recognition. Thus, the hierarchical heatmap-based and hierarchical DensePose representations attain Rank-1 accuracy of 82.2% and 92.0%, respectively, on the cross-view setup of CASIA-B, and 99.3% and 99.8%, respectively, on TUM-GAID, establishing a new benchmark for pose-based methods. Source code is available at https://github.com/Nico-Cubero/PoseGaitGL .

Object detection in video is an essential computer vision task. Consequently, many efforts have been devoted to developing precise and fast deep-learning models for this task. These models are commonly deployed on discrete and powerful GPU devices to meet both frame rate performance and detection accuracy requirements. Furthermore, model training is usually performed in a strongly supervised way so that samples must be previously labelled by humans using a slow and costly process. In this paper, we develop a real-time implementation for unsupervised object detection in video employing a low-power device. We improve typical approaches for object detection using information supplied by optical flow to detect moving objects. Besides, we use an unsupervised clustering algorithm to group similar detections that avoid manual object labelling. Finally, we propose a methodology to optimize the deployment of our resulting framework on an embedded heterogeneous platform. Thus, we illustrate how all the computational resources of a Jetson AGX Xavier (CPU, GPU, and DLAs) can be used to fulfil frame rate, accuracy, and energy consumption requirements. Three different data representations (FP32, FP16 and INT8) are studied for the pipeline networks in order to evaluate the impact of all of them in our pipeline. Obtained results show that our proposed optimizations can improve up to 23 . 6 × energy consumption and 32 . 2 × execution time with respect to the non-optimized pipeline without penalizing the original mAP (59.44). This computational complexity reduction is achieved through knowledge distillation, using FP16 data precision, and deploying concurrent tasks in different computing units.

Many studies have shown that gait recognition can be used to identify humans at a long distance, with promising results on current datasets. However, those datasets are collected under controlled situations and predefined conditions, which limits the extrapolation of the results to unconstrained situations in which the subjects walk freely in scenes. To cover this gap, we release a novel real-scene gait dataset (ReSGait), which is the first dataset collected in unconstrained scenarios with freely moving subjects and not controlled environmental parameters. Overall, our dataset is composed of 172 subjects and 870 video sequences, recorded over 15 months. Video sequences are labeled with gender, clothing, carrying conditions, taken walking route, and whether mobile phones were used or not. Therefore, the main characteristics of our dataset that differentiate it from other datasets are as follows: (i) uncontrolled real-life scenes and (ii) long recording time. Finally, we empirically assess the difficulty of the proposed dataset by evaluating state-of-the-art gait approaches for silhouette and pose modalities. The results reveal an accuracy of less than 35%, showing the inherent level of difficulty of our dataset compared to other current datasets, in which accuracies are higher than 90%. Thus, our proposed dataset establishes a new level of difficulty in the gait recognition problem, much closer to real life.

Multimodal systems for gait recognition have gained a lot of attention. However, there is a clear gap in the study of missing modalities, which represents real-life scenarios where sensors fail or data get corrupted. Here, we investigate how to handle missing modalities for gait recognition. We propose a single and flexible framework that uses a variable number of input modalities. For each modality, it consists of a branch and a binary unit indicating whether the modality is available; these are gated and merged together. Finally, it generates a single and compact ‘multimodal’ gait signature that encodes biometric information of the input. Our framework outperforms the state of the art on TUM-GAID and extensive experiments reveal its effectiveness for handling missing modalities even in the multiview setup of CASIA-B. The code is available online: https://github.com/avagait/gaitmiss.

Gait recognition systems typically rely solely on silhouettes for extracting gait signatures. Nevertheless, these approaches struggle with changes in body shape and dynamic backgrounds; a problem that can be alleviated by learning from multiple modalities. However, in many real-life systems some modalities can be missing, and therefore most existing multimodal frameworks fail to cope with missing modalities. To tackle this problem, in this work, we propose UGaitNet, a unifying framework for gait recognition, robust to missing modalities. UGaitNet handles and mingles various types and combinations of input modalities, i.e. pixel gray value, optical flow, depth maps, and silhouettes, while being camera agnostic. We evaluate UGaitNet on two public datasets for gait recognition: CASIA-B and TUM-GAID, and show that it obtains compact and state-of-the-art gait descriptors when leveraging multiple or missing modalities. Finally, we show that UGaitNet with optical flow and grayscale inputs achieves almost perfect (98.9%) recognition accuracy on CASIA-B (same-view “normal”) and 100% on TUM-GAID (“ellapsed time”). Code will be available.

Gait is a popular biometric pattern used for identifying people based on their way of walking. Traditionally, gait recognition approaches based on deep learning are trained using the whole training dataset. In fact, if new data (classes, view-points, walking conditions, etc.) need to be included, it is necessary to re-train again the model with old and new data samples. In this paper, we propose iLGaCo, the first incremental learning approach of covariate factors for gait recognition, where the deep model can be updated with new information without re-training it from scratch by using the whole dataset. Instead, our approach performs a shorter training process with the new data and a small subset of previous samples. This way, our model learns new information while retaining previous knowledge. We evaluate iLGaCo on CASIA-B dataset in two incremental ways: adding new view-points and adding new walking conditions. In both cases, our results are close to the classical `training-from-scratch' approach, obtaining a marginal drop in accuracy ranging from 0.2% to 1.2%, what shows the efficacy of our approach. In addition, the comparison of iLGaCo with other incremental learning methods, such as LwF and iCarl, shows a significant improvement in accuracy, between 6% and 15% depending on the experiment.

Summary Deep Learning (DL) applications are gaining momentum in the realm of Artificial Intelligence, particularly after GPUs have demonstrated remarkable skills for accelerating their challenging computational requirements. Within this context, Convolutional Neural Network (CNN) models constitute a representative example of success on a wide set of complex applications, particularly on datasets where the target can be represented through a hierarchy of local features of increasing semantic complexity. In most of the real scenarios, the roadmap to improve results relies on CNN settings involving brute force computation, and researchers have lately proven Nvidia GPUs to be one of the best hardware counterparts for acceleration. Our work complements those findings with an energy study on critical parameters for the deployment of CNNs on flagship image and video applications, ie, object recognition and people identification by gait, respectively. We evaluate energy consumption on four different networks based on the two most popular ones (ResNet/AlexNet), ie, ResNet (167 layers), a 2D CNN (15 layers), a CaffeNet (25 layers), and a ResNetIm (94 layers) using batch sizes of 64, 128, and 256, and then correlate those with speed‐up and accuracy to determine optimal settings. Experimental results on a multi‐GPU server endowed with twin Maxwell and twin Pascal Titan X GPUs demonstrate that energy correlates with performance and that Pascal may have up to 40% gains versus Maxwell. Larger batch sizes extend performance gains and energy savings, but we have to keep an eye on accuracy, which sometimes shows a preference for small batches. We expect this work to provide a preliminary guidance for a wide set of CNN and DL applications in modern HPC times, where the GFLOPS/w ratio constitutes the primary goal.

The task of identifying people by the way they walk is known as `gait recognition'. Although gait is mainly used for identification, additional tasks as gender recognition or age estimation may be addressed based on gait as well. In such cases, traditional approaches consider those tasks as independent ones, defining separated task-specific features and models for them. This paper shows that by training jointly more than one gait-based tasks, the identification task converges faster than when it is trained independently, and the recognition performance of multi-task models is equal or superior to more complex single-task ones. Our model is a multi-task CNN that receives as input a fixed-length sequence of optical flow channels and outputs several biometric features (identity, gender and age).

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person, obtaining a rich representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [M. Jain, H. Jegou and P. Bouthemy, Better exploiting motion for better action recognition, in Proc. IEEE Conf. Computer Vision Pattern Recognition (CVPR) (2013), pp. 2555–2562.]) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [F. Perronnin, J. Sánchez and T. Mensink, Improving the Fisher kernel for large-scale image classification, in Proc. European Conf. Computer Vision (ECCV) (2010), pp. 143–156]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on ‘CASIA’ dataset [S. Yu, D. Tan and T. Tan, A framework for evaluating the effect of view angle, clothing and carrying condition on gait recognition, in Proc. Int. Conf. Pattern Recognition, Vol. 4 (2006), pp. 441–444]. (parts B and C), ‘TUM GAID’ dataset, [M. Hofmann, J. Geiger, S. Bachmann, B. Schuller and G. Rigoll, The TUM Gait from Audio, Image and Depth (GAID) database: Multimodal recognition of subjects and traits, J. Vis. Commun. Image Represent. 25(1) (2014) 195–206]. ‘CMU MoBo’ dataset [R. Gross and J. Shi, The CMU Motion of Body (MoBo) database, Technical Report CMU-RI-TR-01-18, Robotics Institute (2001)]. and the recent ‘AVA Multiview Gait’ dataset [D. López-Fernández, F. Madrid-Cuevas, A. Carmona-Poyato, M. Marín-Jiménez and R. Muñoz-Salinas, The AVA multi-view dataset for gait recognition, in Activity Monitoring by Multiple Distributed Sensing, Lecture Notes in Computer Science (Springer, 2014), pp. 26–39]. The results show that this new approach achieves state-of-the-art results in the problem of gait recognition, allowing to recognize walking people from diverse viewpoints on single and multiple camera setups, wearing different clothes, carrying bags, walking at diverse speeds and not limited to straight walking paths.

The goal of this paper is to identify individuals by analyzing their gait. Instead of using binary silhouettes as input data (as done in many previous works) we propose and evaluate the use of motion descriptors based on densely sampled short-term trajectories. We take advantage of state-of-the-art people detectors to define custom spatial configurations of the descriptors around the target person. Thus, obtaining a pyramidal representation of the gait motion. The local motion features (described by the Divergence-Curl-Shear descriptor [1]) extracted on the different spatial areas of the person are combined into a single high-level gait descriptor by using the Fisher Vector encoding [2]. The proposed approach, coined Pyramidal Fisher Motion, is experimentally validated on the recent 'AVA Multiview Gait' dataset [3]. The results show that this new approach achieves promising results in the problem of gait recognition.