Fast heuristic method to detect people in frontal depth images

This paper presents a new method for detecting people using only depth images captured by a camera in a frontal position. The approach is based on first detecting all the objects present in the scene and determining their average depth (distance to the camera). Next, for each object, a 3D Region of Interest (ROI) is processed around it in order to determine if the characteristics of the object correspond to the biometric characteristics of a human head. The results obtained using three public datasets captured by three depth sensors with different spatial resolutions and different operation principle (structured light, active stereo vision and Time of Flight) are presented. These results demonstrate that our method can run in realtime using a low-cost CPU platform with a high accuracy, being the processing times smaller than 1 ms per frame for a 512 × 424 image resolution with a precision of 99.26% and smaller than 4 ms per frame for a 1280 × 720 image resolution with a precision of 99.77%

Whole-body Detection, Recognition and Identification at Altitude and Range

In this paper, we address the challenging task of whole-body biometric detection, recognition, and identification at distances of up to 500m and large pitch angles of up to 50 degree. We propose an end-to-end system evaluated on diverse datasets, including the challenging Biometric Recognition and Identification at Range (BRIAR) dataset. Our approach involves pre-training the detector on common image datasets and fine-tuning it on BRIAR's complex videos and images. After detection, we extract body images and employ a feature extractor for recognition. We conduct thorough evaluations under various conditions, such as different ranges and angles in indoor, outdoor, and aerial scenarios. Our method achieves an average F1 score of 98.29% at IoU = 0.7 and demonstrates strong performance in recognition accuracy and true acceptance rate at low false acceptance rates compared to existing models. On a test set of 100 subjects with 444 distractors, our model achieves a rank-20 recognition accuracy of 75.13% and a TAR@1%FAR of 54.09%

Métodos objetivos estadísticos de valoración de la magnitud de interés en base a las trayectorias de dirección de visionado

Aplicat embargament des de la data de defensa fins el dia 1 de gener de 2022In this work, a new method is presented to quantify the attention paid by people to the different objects in their environment. The new metric models the eye to determine the attention given and quantifies that attention at all points in an area of interest. This metric is compared with the current one based on time and it is justified that ours is more in line with human perception. For the calculation of said attention, the concept of oriented trajectory is introduced as the set of positions and orientation angles of the head, of each person of interest and in the time that is of interest. We will justify that only with this data can such care be determined. In the presented method, top view cameras are used as a system to have the highest performance with the minimum number of cameras. Likewise, two methods are analyzed: the 3D method that uses depth information, and a less precise method, 2D, that only uses imaging cameras. This thesis also presents a way of calculating the time metric, a method that is widely used today to verify how many people and in how long an ad has been seen. The form presented by our method allows reducing the number of cameras required, and therefore it is advantageous in terms of the resources required for its implementation. Finally, the results are verified using a camera in the front part of the head simulating the eye and an IMU sensor that measures the angles of the head. In this way, the attention relationship of the objects detected by the camera is determined, and the same attention relationship of the objects obtained by the proposed method.En este trabajo se presenta un nuevo método para cuantificar la atención prestada por las personas en los diferentes objetos de su entorno. La nueva métrica modeliza el ojo para determinar la atención prestada y cuantifica dicha atención en todos los puntos de una zona de interés. Se compara esta métrica con la actual basada en tiempo y se justifica que la nuestra se ajusta más a la percepción humana. Para el cálculo de dicha atención se introduce el concepto de trayectoria orientada como el conjunto de posiciones y de ángulos de orientación de la cabeza, de cada persona de interés y en el tiempo que sea de interés. Justificaremos que solo con estos datos se puede determinar dicha atención. En el método presentado se utilizan cámaras cenitales como sistema de tener las mayores prestaciones con el mínimo número de cámaras. Así mismo se analizan dos métodos: el método 3D que utiliza la información de profundidad, y un método menos preciso, el 2D, que solo utiliza cámaras de imagen. Esta tesis también presenta una forma de calcular la métrica de tiempos, método que se utiliza ampliamente en la actualidad para verificar cuantas personas y en cuánto tiempo se ha visto un anuncio La forma presentada por nuestro método permite reducir el número de cámaras necesarias, y por tanto es ventajosa en cuanto a los recursos que requiere para su implementación. Finalmente se verifican los resultados utilizando una cámara en la parte frontal de la cabeza simulando al ojo y un sensor IMU que mide los ángulos de la cabeza. De esta manera se determina la relación de atención de los objetos detectados por la cámara, y la misma relación de atención de los objetos obtenidos por el método propuesto.Postprint (published version