Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%

DPDnet: A Robust People Detector using Deep Learning with an Overhead Depth Camera

In this paper we propose a method based on deep learning that detects multiple people from a single overhead depth image with high reliability. Our neural network, called DPDnet, is based on two fully-convolutional encoder-decoder neural blocks based on residual layers. The Main Block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution. The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and head position labels. The experimental work shows that DPDNet outperforms state-of-the-art methods, with accuracies greater than 99% in three different publicly available datasets, without retraining not fine-tuning. In addition, the computational complexity of our proposal is independent of the number of people in the scene and runs in real time using conventional GPUs

Detección de personas en imágenes de profundidad mediante redes neuronales convolucionales

El objetivo de este trabajo es el diseño, implementación y evaluación de un sistema de detección de personas en imágenes de profundidad, basado en Redes Neuronales Convolucionales. Se ha construido una red neuronal profunda, con una estructura compleja, basada en una configuración encoder-decoder y en los bloques residuales de la ResNet. Su entrenamiento se ha dividido en dos partes: en la primera se ha utilizado una base de datos que contiene un gran número de datos sintéticos, generados para esta aplicación y en la segunda se ha llevado a cabo un ajuste con un pequeño conjunto de datos reales, evitando así la necesidad de etiquetar manualmente grandes bases de datos. Tras la evaluación del sistema final, se ha obtenido una tasa de aciertos del 85 %, con una precisión del 100% que ha permitido validar el sistema desarrollado. Ante cualquier problema o sugerencia sobre el presente trabajo, por favor contactad con Roberto Martín López .The objective of this work is the design, implementation and evaluation of a people detection system in depth images, based on Convolutional Neural Networks. The built deep network has a complex architecture, based on encoder-decoder configuration and using ResNet residual layers. The training process has been divided in two parts. The first part, using a big dataset of synthetic data. The second part, using a small set of real data, avoiding to manually label big datasets. After the evaluation of the final system, the success rate obtained is 85 %, with an accuracy of 100% that allows to validate the system. If you have problems, suggestions or comments, please forward them to Roberto Martín López <roberto. [email protected]>.Grado en Ingeniería Electrónica de Comunicacione