UA-DETRAC: A New Benchmark and Protocol for Multi-Object Detection and Tracking

In recent years, numerous effective multi-object tracking (MOT) methods are developed because of the wide range of applications. Existing performance evaluations of MOT methods usually separate the object tracking step from the object detection step by using the same fixed object detection results for comparisons. In this work, we perform a comprehensive quantitative study on the effects of object detection accuracy to the overall MOT performance, using the new large-scale University at Albany DETection and tRACking (UA-DETRAC) benchmark dataset. The UA-DETRAC benchmark dataset consists of 100 challenging video sequences captured from real-world traffic scenes (over 140,000 frames with rich annotations, including occlusion, weather, vehicle category, truncation, and vehicle bounding boxes) for object detection, object tracking and MOT system. We evaluate complete MOT systems constructed from combinations of state-of-the-art object detection and object tracking methods. Our analysis shows the complex effects of object detection accuracy on MOT system performance. Based on these observations, we propose new evaluation tools and metrics for MOT systems that consider both object detection and object tracking for comprehensive analysis.Comment: 18 pages, 11 figures, accepted by CVI

Histograma de orientación de gradientes aplicado al seguimiento múltiple de personas basado en video

El seguimiento múltiple de personas en escenas reales es un tema muy importante en el campo de Visión Computacional dada sus múltiples aplicaciones en áreas como en los sistemas de vigilancia, robótica, seguridad peatonal, marketing, etc., además de los retos inherentes que representa la identificación de personas en escenas reales como son la complejidad de la escena misma, la concurrencia de personas y la presencia de oclusiones dentro del video debido a dicha concurrencia. Existen diversas técnicas que abordan el problema de la segmentación de imágenes y en particular la identificación de personas, desde diversas perspectivas; por su parte el presente trabajo tiene por finalidad desarrollar una propuesta basada en Histograma de Orientación de Gradientes (HOG) para el seguimiento múltiple de personas basado en video. El procedimiento propuesto se descompone en las siguientes etapas: Procesamiento de Video, este proceso consiste en la captura de los frames que componen la secuencia de video, para este propósito se usa la librería OpenCV de tal manera que se pueda capturar la secuencia desde cualquier fuente; la siguiente etapa es la Clasificación de Candidatos, esta etapa se agrupa el proceso de descripción de nuestro objeto, que para el caso de este trabajo son personas y la selección de los candidatos, para esto se hace uso de la implementación del algoritmo de HOG; por último la etapa final es el Seguimiento y Asociación, mediante el uso del algoritmo de Kalman Filter, permite determinar las asociaciones de las secuencias de objetos previamente detectados. La propuesta se aplicó sobre tres conjuntos de datos, tales son: TownCentre (960x540px), TownCentre (1920x1080px) y PETS 2009, obteniéndose los resultados para precisión: 94.47%, 90.63% y 97.30% respectivamente. Los resultados obtenidos durante las experimentaciones validan la propuesta del modelo haciendo de esta una herramienta que puede encontrar múltiples campos de aplicación, además de ser una propuesta innovadora a nivel nacional dentro del campo de Vision Computacional.Tesi

Automatic Sign Language Recognition from Image Data

Tato práce se zabývá problematikou automatického rozpoznávání znakového jazyka z obrazových dat. Práce představuje pět hlavních přínosů v oblasti tvorby systému pro rozpoznávání, tvorby korpusů, extrakci příznaků z rukou a obličeje s využitím metod pro sledování pozice a pohybu rukou (tracking) a modelování znaků s využitím menších fonetických jednotek (sub-units). Metody využité v rozpoznávacím systému byly využity i k tvorbě vyhledávacího nástroje "search by example", který dokáže vyhledávat ve videozáznamech podle obrázku ruky. Navržený systém pro automatické rozpoznávání znakového jazyka je založen na statistickém přístupu s využitím skrytých Markovových modelů, obsahuje moduly pro analýzu video dat, modelování znaků a dekódování. Systém je schopen rozpoznávat jak izolované, tak spojité promluvy. Veškeré experimenty a vyhodnocení byly provedeny s vlastními korpusy UWB-06-SLR-A a UWB-07-SLR-P, první z nich obsahuje 25 znaků, druhý 378. Základní extrakce příznaků z video dat byla provedena na nízkoúrovňových popisech obrazu. Lepších výsledků bylo dosaženo s příznaky získaných z popisů vyšší úrovně porozumění obsahu v obraze, které využívají sledování pozice rukou a metodu pro segmentaci rukou v době překryvu s obličejem. Navíc, využitá metoda dokáže interpolovat obrazy s obličejem v době překryvu a umožňuje tak využít metody pro extrakci příznaků z obličeje, které by během překryvu nefungovaly, jako např. metoda active appearance models (AAM). Bylo porovnáno několik různých metod pro extrakci příznaků z rukou, jako např. local binary patterns (LBP), histogram of oriented gradients (HOG), vysokoúrovnové lingvistické příznaky a nové navržená metoda hand shape radial distance function (hRDF). Bylo také zkoumáno využití menších fonetických jednotek, než jsou celé znaky, tzv. sub-units. Pro první krok tvorby těchto jednotek byl navržen iterativní algoritmus, který tyto jednotky automaticky vytváří analýzou existujících dat. Bylo ukázáno, že tento koncept je vhodný pro modelování a rozpoznávání znaků. Kromě systému pro rozpoznávání je v práci navržen a představen systém "search by example", který funguje jako vyhledávací systém pro videa se záznamy znakového jazyka a může být využit například v online slovnících znakového jazyka, kde je v současné době složité či nemožné v takovýchto datech vyhledávat. Tento nástroj využívá metody, které byly použity v rozpoznávacím systému. Výstupem tohoto vyhledávacího nástroje je seřazený seznam videí, které obsahují stejný nebo podobný tvar ruky, které zadal uživatel, např. přes webkameru.Katedra kybernetikyObhájenoThis thesis addresses several issues of automatic sign language recognition, namely the creation of vision based sign language recognition framework, sign language corpora creation, feature extraction, making use of novel hand tracking with face occlusion handling, data-driven creation of sub-units and "search by example" tool for searching in sign language corpora using hand images as a search query. The proposed sign language recognition framework, based on statistical approach incorporating hidden Markov models (HMM), consists of video analysis, sign modeling and decoding modules. The framework is able to recognize both isolated signs and continuous utterances from video data. All experiments and evaluations were performed on two own corpora, UWB-06-SLR-A and UWB-07-SLR-P, the first containing 25 signs and second 378. As a baseline feature descriptors, low level image features are used. It is shown that better performance is gained by higher level features that employ hand tracking, which resolve occlusions of hands and face. As a side effect, the occlusion handling method interpolates face area in the frames during the occlusion and allows to use face feature descriptors that fail in such a case, for instance features extracted from active appearance models (AAM) tracker. Several state-of-the-art appearance-based feature descriptors were compared for tracked hands, such as local binary patterns (LBP), histogram of oriented gradients (HOG), high-level linguistic features or newly proposed hand shape radial distance function (denoted as hRDF) that enhances the feature description of hand-shape like concave regions. The concept of sub-units, that uses HMM models based on linguistic units smaller than whole sign and covers inner structures of the signs, was investigated in the proposed iterative method that is a first required step for data-driven construction of sub-units, and shows that such a concept is suitable for sign modeling and recognition tasks. Except of experiments in the sign language recognition, additional tool \textit{search by example} was created and evaluated. This tool is a search engine for sign language videos. Such a system can be incorporated into an online sign language dictionary where it is difficult to search in the sign language data. This proposed tool employs several methods which were examined in the sign language recognition task and allows to search in the video corpora based on an user-given query that consists of one or multiple images of hands. As a result, an ordered list of videos that contain the same or similar hand configurations is returned

A Comprehensive Performance Evaluation of Deformable Face Tracking "In-the-Wild"

Recently, technologies such as face detection, facial landmark localisation and face recognition and verification have matured enough to provide effective and efficient solutions for imagery captured under arbitrary conditions (referred to as "in-the-wild"). This is partially attributed to the fact that comprehensive "in-the-wild" benchmarks have been developed for face detection, landmark localisation and recognition/verification. A very important technology that has not been thoroughly evaluated yet is deformable face tracking "in-the-wild". Until now, the performance has mainly been assessed qualitatively by visually assessing the result of a deformable face tracking technology on short videos. In this paper, we perform the first, to the best of our knowledge, thorough evaluation of state-of-the-art deformable face tracking pipelines using the recently introduced 300VW benchmark. We evaluate many different architectures focusing mainly on the task of on-line deformable face tracking. In particular, we compare the following general strategies: (a) generic face detection plus generic facial landmark localisation, (b) generic model free tracking plus generic facial landmark localisation, as well as (c) hybrid approaches using state-of-the-art face detection, model free tracking and facial landmark localisation technologies. Our evaluation reveals future avenues for further research on the topic.Comment: E. Antonakos and P. Snape contributed equally and have joint second authorshi

Real-Time Human Detection Using Deep Learning on Embedded Platforms: A Review

The detection of an object such as a human is very important for image understanding in the field of computer vision. Human detection in images can provide essential information for a wide variety of applications in intelligent systems. In this paper, human detection is carried out using deep learning that has developed rapidly and achieved extraordinary success in various object detection implementations. Recently, several embedded systems have emerged as powerful computing boards to provide high processing capabilities using the graphics processing unit (GPU). This paper aims to provide a comprehensive survey of the latest achievements in this field brought about by deep learning techniques in the embedded platforms. NVIDIA Jetson was chosen as a low power system designed to accelerate deep learning applications. This review highlights the performance of human detection models such as PedNet, multiped, SSD MobileNet V1, SSD MobileNet V2, and SSD inception V2 on edge computing. This survey aims to provide an overview of these methods and compare their performance in accuracy and computation time for real-time applications. The experimental results show that the SSD MobileNet V2 model provides the highest accuracy with the fastest computation time compared to other models in our video datasets with several scenarios

Compound Models for Vision-Based Pedestrian Recognition

This thesis addresses the problem of recognizing pedestrians in video images acquired from a moving camera in real-world cluttered environments. Instead of focusing on the development of novel feature primitives or pattern classifiers, we follow an orthogonal direction and develop feature- and classifier-independent compound techniques which integrate complementary information from multiple image-based sources with the objective of improved pedestrian classification performance. After establishing a performance baseline in terms of a thorough experimental study on monocular pedestrian recognition, we investigate the use of multiple cues on module-level. A motion-based focus of attention stage is proposed based on a learned probabilistic pedestrian-specific model of motion features. The model is used to generate pedestrian localization hypotheses for subsequent shape- and texture-based classification modules. In the remainder of this work, we focus on the integration of complementary information directly into the pattern classification step. We present a combination of shape and texture information by means of pose-specific generative shape and texture models. The generative models are integrated with discriminative classification models by utilizing synthesized virtual pedestrian training samples from the former to enhance the classification performance of the latter. Both models are linked using Active Learning to guide the training process towards informative samples. A multi-level mixture-of-experts classification framework is proposed which involves local pose-specific expert classifiers operating on multiple image modalities and features. In terms of image modalities, we consider gray-level intensity, depth cues derived from dense stereo vision and motion cues arising from dense optical flow. We furthermore employ shape-based, gradient-based and texture-based features. The mixture-of-experts formulation compares favorably to joint space approaches, in view of performance and practical feasibility. Finally, we extend this mixture-of-experts framework in terms of multi-cue partial occlusion handling and the estimation of pedestrian body orientation. Our occlusion model involves examining occlusion boundaries which manifest in discontinuities in depth and motion space. Occlusion-dependent weights which relate to the visibility of certain body parts focus the decision on unoccluded body components. We further apply the pose-specific nature of our mixture-of-experts framework towards estimating the density of pedestrian body orientation from single images, again integrating shape and texture information. Throughout this work, particular emphasis is laid on thorough performance evaluation both regarding methodology and competitive real-world datasets. Several datasets used in this thesis are made publicly available for benchmarking purposes. Our results indicate significant performance boosts over state-of-the-art for all aspects considered in this thesis, i.e. pedestrian recognition, partial occlusion handling and body orientation estimation. The pedestrian recognition performance in particular is considerably advanced; false detections at constant detection rates are reduced by significantly more than an order of magnitude

Pedestrian detection in far infrared images

Detection of people in images is a relatively new field of research, but has been widely accepted. The applications are multiple, such as self-labeling of large databases, security systems and pedestrian detection in intelligent transportation systems. Within the latter, the purpose of a pedestrian detector from a moving vehicle is to detect the presence of people in the path of the vehicle. The ultimate goal is to avoid a collision between the two. This thesis is framed with the advanced driver assistance systems, passive safety systems that warn the driver of conditions that may be adverse. An advanced driving assistance system module, aimed to warn the driver about the presence of pedestrians, using computer vision in thermal images, is presented in this thesis. Such sensors are particularly useful under conditions of low illumination.The document is divided following the usual parts of a pedestrian detection system: development of descriptors that define the appearance of people in these kind of images, the application of these descriptors to full-sized images and temporal tracking of pedestrians found. As part of the work developed in this thesis, database of pedestrians in the far infrared spectrum is presented. This database has been used in developing an evaluation of pedestrian detection systems as well as for the development of new descriptors. These descriptors use techniques for the systematic description of the shape of the pedestrian as well as methods to achieve invariance to contrast, illumination or ambient temperature. The descriptors are analyzed and modified to improve their performance in a detection problem, where potential candidates are searched for in full size images. Finally, a method for tracking the detected pedestrians is proposed to reduce the number of miss-detections that occurred at earlier stages of the algorithm. --La detección de personas en imágenes es un campo de investigación relativamente nuevo, pero que ha tenido una amplia acogida. Las aplicaciones son múltiples, tales como auto-etiquetado de grandes bases de datos, sistemas de seguridad y detección de peatones en sistemas inteligentes de transporte. Dentro de este último, la detección de peatones desde un vehículo móvil tiene como objetivo detectar la presencia de personas en la trayectoria del vehículo. EL fin último es evitar una colisión entre ambos. Esta tesis se enmarca en los sistemas avanzados de ayuda a la conducción; sistemas de seguridad pasivos, que advierten al conductor de condiciones que pueden ser adversas. En esta tesis se presenta un módulo de ayuda a la conducción destinado a advertir de la presencia de peatones, mediante el uso de visión por computador en imágenes térmicas. Este tipo de sensores resultan especialmente útiles en condiciones de baja iluminación. El documento se divide siguiendo las partes habituales de una sistema de detección de peatones: desarrollo de descriptores que defina la apariencia de las personas en este tipo de imágenes, la aplicación de estos en imágenes de tamano completo y el seguimiento temporal de los peatones encontrados. Como parte del trabajo desarrollado en esta tesis se presenta una base de datos de peatones en el espectro infrarrojo lejano. Esta base de datos ha sido utilizada para desarrollar una evaluación de sistemas de detección de peatones, así como para el desarrollo de nuevos descriptores. Estos integran técnicas para la descripción sistemática de la forma del peatón, así como métodos para la invariancia al contraste, la iluminación o la temperatura externa. Los descriptores son analizados y modificados para mejorar su rendimiento en un problema de detección, donde se buscan posibles candidatos en una imagen de tamano completo. Finalmente, se propone una método de seguimiento de los peatones detectados para reducir el número de fallos que se hayan producido etapas anteriores del algoritmo