Full Reference Objective Quality Assessment for Reconstructed Background Images

With an increased interest in applications that require a clean background image, such as video surveillance, object tracking, street view imaging and location-based services on web-based maps, multiple algorithms have been developed to reconstruct a background image from cluttered scenes. Traditionally, statistical measures and existing image quality techniques have been applied for evaluating the quality of the reconstructed background images. Though these quality assessment methods have been widely used in the past, their performance in evaluating the perceived quality of the reconstructed background image has not been verified. In this work, we discuss the shortcomings in existing metrics and propose a full reference Reconstructed Background image Quality Index (RBQI) that combines color and structural information at multiple scales using a probability summation model to predict the perceived quality in the reconstructed background image given a reference image. To compare the performance of the proposed quality index with existing image quality assessment measures, we construct two different datasets consisting of reconstructed background images and corresponding subjective scores. The quality assessment measures are evaluated by correlating their objective scores with human subjective ratings. The correlation results show that the proposed RBQI outperforms all the existing approaches. Additionally, the constructed datasets and the corresponding subjective scores provide a benchmark to evaluate the performance of future metrics that are developed to evaluate the perceived quality of reconstructed background images.Comment: Associated source code: https://github.com/ashrotre/RBQI, Associated Database: https://drive.google.com/drive/folders/1bg8YRPIBcxpKIF9BIPisULPBPcA5x-Bk?usp=sharing (Email for permissions at: ashrotreasuedu

Moving Object Detection based on RGBD Information

This thesis is targeting the Moving Object Detection topic, more specifically, the Background Subtraction. In this study, we proposed two approaches using color and depth information to solve the background subtraction. The following two paragraphs will give a brief abstract for each approach. In this research study, we propose a framework for improving traditional Background Subtraction techniques. This framework is based on two data types: color and depth; it stands for obtaining preliminary results of the background segmentation using Depth and RGB channels independently, then using an algorithm to fuse them to create the final results. The experiments on the SBM-RGBD dataset using four methods: ViBe, LOBSTER, SuBSENSE, and PAWCS, proved that the proposed framework achieves an impressive performance compared to the original RGB-based techniques from the state-of-the-art. This dissertation also proposes a novel deep learning model called Deep Multi-Scale Network (DMSN) for Background Subtraction. This convolutional neural network is built to use RGB color channels and Depth maps as inputs with which it can fuse semantic and spatial information. Compared with previous Deep Learning Background Subtraction techniques that lack information due to their use of only RGB channels, our RGBD version can overcome most of the drawbacks, especially in some particular challenges. Further, this study introduces a new protocol for the SBM-RGBD dataset regarding scene-independent evaluation, dedicated to Deep Learning methods to set up a competitive platform that includes more challenging situations. The proposed method proved its efficiency in solving the background subtraction in complex problems at different levels. The experimental results verify that the proposed work outperforms the state-of-the-art on SBM-RGBD and GSM datasets

Méthodes de vision à la motion et leurs applications

La détection de mouvement est une opération de base souvent utilisée en vision par ordinateur, que ce soit pour la détection de piétons, la détection d’anomalies, l’analyse de scènes vidéo ou le suivi d’objets en temps réel. Bien qu’un très grand nombre d’articles ait été publiés sur le sujet, plusieurs questions restent en suspens. Par exemple, il n’est toujours pas clair comment détecter des objets en mouvement dans des vidéos contenant des situations difficiles à gérer comme d'importants mouvements de fonds et des changements d’illumination. De plus, il n’y a pas de consensus sur comment quantifier les performances des méthodes de détection de mouvement. Aussi, il est souvent difficile d’incorporer de l’information de mouvement à des opérations de haut niveau comme par exemple la détection de piétons. Dans cette thèse, j’aborde quatre problèmes en lien avec la détection de mouvement: 1. Comment évaluer efficacement des méthodes de détection de mouvement? Pour répondre à cette question, nous avons mis sur pied une procédure d’évaluation de telles méthodes. Cela a mené à la création de la plus grosse base de données 100\% annotée au monde dédiée à la détection de mouvement et organisé une compétition internationale (CVPR 2014). J’ai également exploré différentes métriques d’évaluation ainsi que des stratégies de combinaison de méthodes de détection de mouvement. 2. L’annotation manuelle de chaque objet en mouvement dans un grand nombre de vidéos est un immense défi lors de la création d’une base de données d’analyse vidéo. Bien qu’il existe des méthodes de segmentation automatiques et semi-automatiques, ces dernières ne sont jamais assez précises pour produire des résultats de type “vérité terrain”. Pour résoudre ce problème, nous avons proposé une méthode interactive de segmentation d’objets en mouvement basée sur l’apprentissage profond. Les résultats obtenus sont aussi précis que ceux obtenus par un être humain tout en étant 40 fois plus rapide. 3. Les méthodes de détection de piétons sont très souvent utilisées en analyse de la vidéo. Malheureusement, elles souffrent parfois d’un grand nombre de faux positifs ou de faux négatifs tout dépendant de l’ajustement des paramètres de la méthode. Dans le but d’augmenter les performances des méthodes de détection de piétons, nous avons proposé un filtre non linéaire basée sur la détection de mouvement permettant de grandement réduire le nombre de faux positifs. 4. L’initialisation de fond ({\em background initialization}) est le processus par lequel on cherche à retrouver l’image de fond d’une vidéo sans les objets en mouvement. Bien qu’un grand nombre de méthodes ait été proposé, tout comme la détection de mouvement, il n’existe aucune base de donnée ni procédure d’évaluation pour de telles méthodes. Nous avons donc mis sur pied la plus grosse base de données au monde pour ce type d’applications et avons organisé une compétition internationale (ICPR 2016).Abstract : Motion detection is a basic video analytic operation on which many high-level computer vision tasks are built upon, e.g., pedestrian detection, anomaly detection, scene understanding and object tracking strategies. Even though a large number of motion detection methods have been proposed in the last decades, some important questions are still unanswered, including: (1) how to separate the foreground from the background accurately even under extremely challenging circumstances? (2) how to evaluate different motion detection methods? And (3) how to use motion information extracted by motion detection to help improving high-level computer vision tasks? In this thesis, we address four problems related to motion detection: 1. How can we benchmark (and on which videos) motion detection method? Current datasets are either too small with a limited number of scenarios, or only provide bounding box ground truth that indicates the rough location of foreground objects. As a solution, we built the largest and most objective motion detection dataset in the world with pixel accurate ground truth to evaluate and compare motion detection methods. We also explore various evaluation metrics as well as different combination strategies. 2. Providing pixel accurate ground truth is a huge challenge when building a motion detection dataset. While automatic labeling methods suffer from a too large false detection rate to be used as ground truth, manual labeling of hundreds of thousands of frames is extremely time consuming. To solve this problem, we proposed an interactive deep learning method for segmenting moving objects from videos. The proposed method can reach human-level accuracies while lowering the labeling time by a factor of 40. 3. Pedestrian detectors always suffer from either false positive detections or false negative detections all depending on the parameter tuning. Unfortunately, manual adjustment of parameters for a large number of videos is not feasible in practice. In order to make pedestrian detectors more robust on a large variety of videos, we combined motion detection with various state-of-the-art pedestrian detectors. This is done by a novel motion-based nonlinear filtering process which improves detectors by a significant margin. 4. Scene background initialization is the process by which a method tries to recover the RGB background image of a video without foreground objects in it. However, one of the reasons that background modeling is challenging is that there is no good dataset and benchmarking framework to estimate the performance of background modeling methods. To fix this problem, we proposed an extensive survey as well as a novel benchmarking framework for scene background initialization