REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed

(Certified) Humane Violence? Animal Production, the Ambivalence of Humanizing the Inhumane, and What International Humanitarian Law Has to Do with It

The chapter draws a comparison with the self-certifying of production methods as ‘humane’ or animal-friendly in the labelling of animal products—that is, according to companies’ own self-imposed codes of conduct. It likens the idea of humanizing animal slaughter, factory farms, and other forms of production to the notion of humanizing warfare. Like international humanitarian law (IHL), animal welfare law is marked by the tension inherent in its attempt to humanize innately inhumane practices. Given these parallels, the analysis of animal welfare law might benefit from existing insights into the potential and limits of IHL. Both areas of law endorse a principle of ‘humanity’ while arguably facilitating and legitimizing the use of violence, and might thereby ultimately perpetuate the suffering of living beings. The implicit justification of violence percolating from the IHL-like animal ‘protection’ laws could only be outweighed by complementing this body of law with a ius contra bellum for animals

Real Time Pedestrian Detection-based Faster HOG/DPM and Deep Learning Approaches

International audienc

THE INFLUENCE OF SURFACES ON ACCELERATION AND DECELERATION CAPACITY AND RATING OF PERCEIVED EXERTION

Megan A. Gordon, Brandi E. Decoux, FACSM, Bovorn Sirikul, FACSM. Southeastern Louisiana University, Hammond, LA. BACKGROUND: Soccer is a high intensity sport requiring frequent changes in speed and direction. Though the sport is traditionally played on natural grass (NG), it has become more prominent in recent years for artificial turf (AT) to be considered as an acceptable alternative. Consequently, there has been an increase in studies focused on the impact of different playing surfaces on injury rates and player perception. Additional research, however, is needed to better understand how these surfaces influence the mechanical demands observed during performance as well as the perceived physiological demands. Thus, the purpose of this study is to investigate acceleration and deceleration profiles and rating of perceived exertion (RPE) among NCAA Division I women’s soccer players on NG vs AT. METHODS: Participants between the ages of 18 to 23 years will be recruited from a Division I women’s soccer team. Data will be collected across a competitive season using TITAN 1+ GPS wearable sensors and the TITAN Athlete App (Integrated bionics, Houston, TX, USA). Each participant will wear a GPS sensor within a fitted undergarment vest under their jersey during each match, and afterwards, each will report their RPE in the app using their personal smartphone. At each match, the type of playing surface (i.e., NG or AT) will be documented and wet bulb globe temperature (WBGT) will be recorded at 15 minute intervals throughout each 90-minute event to account for the effect of temperature, humidity, and solar radiation on the players. Data collected with the GPS sensors and the app will automatically be compiled in the TITAN Session Explorer software from which the accelerations, decelerations, and RPE collected for each game will be exported for analysis. Data on NG vs AT will be analyzed using repeated measures t-tests. Pearson correlation tests will also be conducted to measure the strength of the relationship between RPE and accelerations, decelerations, and WBGT. ANTICIPATED RESULTS: It is hypothesized that there will be a greater number of accelerations and decelerations on AT than NG, and RPE will be higher on AT than NG. In addition, it is also hypothesized that accelerations, decelerations, and WBGT will be correlated to RPE

REAL TIME PEDESTRIAN DETECTION-BASED FASTER HOG/DPM AND DEEP LEARNING APPROACH

International audienceThe work presented aims to show the feasibility of scientific and technological concepts in embedded vision dedicated to the extraction of image characteristics allowing the detection and the recognition/localization of objects. Object and pedestrian detection are carried out by two methods: 1. Classical image processing approach, which are improved with Histogram Oriented Gradient (HOG) and Deformable Part Model (DPM) based detection and pattern recognition. We present how we have improved the HOG/DPM approach to make pedestrian detection as a real time task by reducing calculation time. The developed approach allows us not only a pedestrian detection but also calculates the distance between pedestrians and vehicle. 2. Pedestrian detection based Artificial Intelligence (AI) approaches such as Deep Learning (DL). This work has first been validated on a closed circuit and subsequently under real traffic conditions through mobile platforms (mobile robot, drone and vehicles). Several tests have been carried out in the city center of Rouen in order to validate the platform developed

Lightweight convolutional neural network for real-time 3D object detection in road and railway environments

International audienceFor smart mobility, and autonomous vehicles (AV), it is necessary to have a very precise perception of the environment to guarantee reliable decision-making, and to be able to extend the results obtained for the road sector to other areas such as rail. To this end, we introduce a new single-stage monocular real-time 3D object detection convolutional neural network (CNN) based on YOLOv5, dedicated to smart mobility applications for both road and rail environments. To perform the 3D parameter regression, we replace YOLOv5’s anchor boxes with our hybrid anchor boxes. Our method is available in different model sizes such as YOLOv5: small, medium, and large. The new model that we propose is optimized for real-time embedded constraints (lightweight, speed, and accuracy) that takes advantage of the improvement brought by split attention (SA) convolutions called small split attention model (Small-SA). To validate our CNN model, we also introduce a new virtual dataset for both road and rail environments by leveraging the video game Grand Theft Auto V (GTAV). We provide extensive results of our different models on both KITTI and our own GTAV datasets. Through our results, we show that our method is the fastest available 3D object detection with accuracy results close to state-of-the-art methods on the KITTI road dataset. We further demonstrate that the pre-training process on our GTAV virtual dataset improves the accuracy on real datasets such as KITTI, thus allowing our method to obtain an even greater accuracy than state-of-the-art approaches with 16.16% 3D average precision on hard car detection with inference time of 11.1 ms/image on an RTX 3080 GPU.Pour la mobilité intelligente, et les véhicules autonomes (VA), il est nécessaire d'avoir une perception très précise de l'environnement pour garantir une prise de décision fiable, et de pouvoir étendre les résultats obtenus pour le secteur routier à d'autres domaines comme le ferroviaire. À cette fin, nous introduisons un nouveau réseau de neurones convolutif (CNN) monoculaire de détection d'objets 3D en temps réel basé sur YOLOv5, dédié aux applications de mobilité intelligente pour les environnements routiers et ferroviaires. Pour effectuer la régression des paramètres 3D, nous remplaçons les boîtes d'ancrage de YOLOv5 par nos boîtes d'ancrage hybrides. Notre méthode est disponible dans différentes tailles de modèles comme YOLOv5 : petit, moyen et grand. Le nouveau modèle que nous proposons est optimisé pour les contraintes de l'embarqué en temps réel (légèreté, vitesse et précision) qui tire profit de l'amélioration apportée par les convolutions d'attention fractionnée (SA) appelé petit modèle d'attention fractionnée (Small-SA). Pour valider notre modèle CNN, nous introduisons également un nouvel ensemble de données virtuelles pour les environnements routiers et ferroviaires en exploitant le jeu vidéo Grand Theft Auto V (GTAV). Nous fournissons des résultats détaillés de nos différents modèles à la fois sur KITTI et sur nos propres jeux de données GTAV. Nous montrons que notre méthode est la plus rapide pour la détection d'objets en 3D, avec une précision proche de celle des méthodes les plus avancées sur le jeu de données KITTI. Nous démontrons également que le processus de pré-entraînement sur notre jeu de données virtuel GTAV améliore la précision sur les jeux de données réels tels que KITTI, permettant ainsi à notre méthode d'obtenir une précision encore plus grande que les approches de l'état de l'art avec 16,16% de précision moyenne 3D sur la détection de voitures avec un temps d'inférence de 11,1 ms/image sur un GPU RTX 3080

INFLUENCE OF NATURAL GRASS AND ARTIFICIAL TURF SURFACES ON ATHLETE PERFORMANCE AND PERCEIVED PERFORMANCE SATISFACTION

Hannah Reck1, Brandi Decoux1, Samantha Carson1, Daniel B. Hollander1, Megan Gordon1, Bovorn Sirikul1, Christopher Wilburn2, Wendi Weimar2. 1Southeastern Louisiana University, Hammond, LA. 2Auburn University, Auburn, AL. BACKGROUND: Examination of athlete performance and perceptions across different playing surfaces has provided useful information to better understand athlete preferences, tactical alterations, and focus areas for industry/material science developers. However, much of the previous research on natural grass (NG) and artificial turf (AT) surfaces has been limited in scope to comparisons of only performance measures or only perceptual ratings. Additionally, fewer studies have assessed both performance and perception across multiple AT surfaces and NG within the same project. Thus, the purpose of this study was to investigate the influence of NG and different AT playing surfaces on athlete performance and perceived performance satisfaction. METHODS: Seventeen male participants (age: 23.1 ± 2.9 years; height: 1.81 ± 0.06 m; mass: 77.8 ± 9.9 kg) completed three 20-yard sprint trials and three change of direction (CoD) trials (i.e., 5-10-5 agility) on four playing surfaces-one NG surface and three AT surfaces with varying structural components. After completion of all performance tests, each participant then responded to a visual analogue scale (VAS) questionnaire for each surface regarding their satisfaction with the surface’s grip/traction and softness/compliance as well as their ability to change direction and accelerate. Friedman tests were conducted to compare sprint time, CoD time, CoD deficit, and the VAS scores across all surfaces. RESULTS: There were statistically significant differences detected for CoD deficit (χ2(3)= 9.071, p= 0.028), acceleration VAS score (χ2(3)= 10.089, p= 0.018), and softness/compliance VAS score (χ2(3)= 10.804, p= 0.013). Post hoc Wilcoxon signed-rank tests with a Bonferroni correction (a=.0125) revealed that CoD deficit on the third AT surface was larger than on NG (p= .008), the third AT was ranked higher for acceleration VAS score than the second AT (p=.003), and the third AT was ranked lower than NG for softness/compliance VAS score (p=.002). CONCLUSION: Interestingly, the participants in this study perceived the third AT to be a harder surface that they could accelerate better on, and yet CoD deficit, a measure that is improved by enhanced acceleration ability, was compromised on this surface compared to NG. These findings suggest that perceptions of the performance-related characteristics of AT and actual performance are not always congruent. KEYWORDS: Perception, Performance, Artificial turf, Natural Gras