Are object detection assessment criteria ready for maritime computer vision?

Maritime vessels equipped with visible and infrared cameras can complement other conventional sensors for object detection. However, application of computer vision techniques in maritime domain received attention only recently. The maritime environment offers its own unique requirements and challenges. Assessment of the quality of detections is a fundamental need in computer vision. However, the conventional assessment metrics suitable for usual object detection are deficient in the maritime setting. Thus, a large body of related work in computer vision appears inapplicable to the maritime setting at the first sight. We discuss the problem of defining assessment metrics suitable for maritime computer vision. We consider new bottom edge proximity metrics as assessment metrics for maritime computer vision. These metrics indicate that existing computer vision approaches are indeed promising for maritime computer vision and can play a foundational role in the emerging field of maritime computer vision

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD framework is usually suboptimal since it is not end-to-end, i.e., it considers the task as detection and tracking, but not jointly. To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames. Learning is then driven by the reconstruction error through backpropagation. We further propose a Reprioritized Attentive Tracking to improve the robustness of data association. Experiments conducted on both synthetic and real video datasets show the potential of the proposed model. Our project page is publicly available at: https://github.com/zhen-he/tracking-by-animationComment: CVPR 201

Online and Batch Supervised Background Estimation via L1 Regression

We propose a surprisingly simple model for supervised video background estimation. Our model is based on $\ell_1$ regression. As existing methods for $\ell_1$ regression do not scale to high-resolution videos, we propose several simple and scalable methods for solving the problem, including iteratively reweighted least squares, a homotopy method, and stochastic gradient descent. We show through extensive experiments that our model and methods match or outperform the state-of-the-art online and batch methods in virtually all quantitative and qualitative measures

Deep background subtraction of thermal and visible imagery for redestrian detection in videos

In this paper, we introduce an efficient framework to subtract the background from both visible and thermal imagery for pedestrians’ detection in the urban scene. We use a deep neural network (DNN) to train the background subtraction model. For the training of the DNN, we first generate an initial background map and then employ randomly 5% video frames, background map, and manually segmented ground truth. Then we apply a cognition-based post-processing to further smooth the foreground detection result. We evaluate our method against our previous work and 11 recently widely cited method on three challenge video series selected from a publicly available color-thermal benchmark dataset OCTBVS. Promising results have been shown that the proposed DNN-based approach can successfully detect the pedestrians with good shape in most scenes regardless of illuminate changes and occlusion problem

UHD映像のための前景物体検出の高速化

早大学位記番号:新7460早稲田大

UHD映像のための前景物体検出の高速化

早大学位記番号:新7460早稲田大

Detección de sombras en secuencias de video-seguridad

El principal objetivo de este proyecto n de carrera es el diseño e impelentación de un algoritmo de detección de sombras. Muchas de las aplicaciones utilizadas en computer vision, como la video-vigilancia requieren la detección y seguimiento de objetos donde las técnicas de substracción de fondo son comunmente usadas para la segmentación de frente/fondo. Sin embargo, las sombras proyectadas por objetos del frente que se encuentran en movimiento a menudo provocan errores de detección en dichas aplicaciones. Para afrontar este problema, este trabajo propone el diseño de un algoritmo de detección de sombras, explotando la información de color de diferentes espacios por medio del cálculo de ratios entre los píxeles que se encuentran bajo regiones de sombra y los pixeles pertenecientes al fondo. Para este propósito primero se ha estudiado, implementado, adaptado y evaluado las principales y más relevantes técnicas de substracción de fondo y métodos de sombra que forman la base de la myoría de los detectores de la bibliografía, poniendo de mani esto sus carencias en cuanto a la detección y eliminación de sombras se re ere. Posteriormente se describirá el algoritmo propuesto explicando cada una de las etapas del proceso llevadas a cabo como son el cálculo de ratios, histogramas, medidas de correlación entre canales y optimización de umbrales y se presentarán los resultados asociados en un capítulo de experimentos, realizando una evaluación comparativa con algunos de los algoritmos encontrados en la bibliografía.The main goal of this master thesis is the design and implementation of a shadow detection algorithm. Many computer vision applications such as video-surveillance require the detection and object tracking where background substraction is commonly applied for background/foreground segmentation. However cast shadows from moving foreground objects usually result in errors for such applications. To address these problems, this work proposes the design and implementation of a shadow detection algorithm, exploiting the colour information by means of calculating the ratios between pixels under shadow regions and background pixels for di erent colour spaces. For this purpose the author rst studied, implemented, adapted and evaluated the main and most relevant techniques of background substraction and shadow methods that form the basis of most detectors in the literature, highlighting the main gaps they present in detecting and removing shadows from image sequences. It is described later the proposed algorithm explaining each of the process steps such us the calculation of ratios, histograms, colour spaces channel correlation and optimization of thresholds. The results associated to every procces of the algorithm will be presented in four experiments, performing a comparative evaluation with some of the algotrithms found in the literature