Foreground segmentation in depth imagery using depth and spatial dynamic models for video surveillance applications

Low-cost systems that can obtain a high-quality foreground segmentation almostindependently of the existing illumination conditions for indoor environments are verydesirable, especially for security and surveillance applications. In this paper, a novelforeground segmentation algorithm that uses only a Kinect depth sensor is proposedto satisfy the aforementioned system characteristics. This is achieved by combininga mixture of Gaussians-based background subtraction algorithm with a new Bayesiannetwork that robustly predicts the foreground/background regions between consecutivetime steps. The Bayesian network explicitly exploits the intrinsic characteristics ofthe depth data by means of two dynamic models that estimate the spatial and depthevolution of the foreground/background regions. The most remarkable contribution is thedepth-based dynamic model that predicts the changes in the foreground depth distributionbetween consecutive time steps. This is a key difference with regard to visible imagery,where the color/gray distribution of the foreground is typically assumed to be constant.Experiments carried out on two different depth-based databases demonstrate that theproposed combination of algorithms is able to obtain a more accurate segmentation of theforeground/background than other state-of-the art approaches

A review of 28 free animal tracking software: current features and limitations

This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1038/s41684-021-00811-1[Abstract]: Well-quantified laboratory studies can provide a fundamental understanding of animal behavior in ecology, ethology and ecotoxicology research. These types of studies require observation and tracking of each animal in well-controlled and defined arenas, often for long timescales. Thus, these experiments produce long time series and a vast amount of data that require the use of software applications to automate the analysis and reduce manual annotation. In this review, we examine 28 free software applications for animal tracking to guide researchers in selecting the software that might best suit a particular experiment. We also review the algorithms in the tracking pipeline of the applications, explain how specific techniques can fit different experiments, and finally, expose each approach’s weaknesses and strengths. Our in-depth review includes last update, type of platform, user-friendliness, off- or online video acquisition, calibration method, background subtraction and segmentation method, species, multiple arenas, multiple animals, identity preservation, manual identity correction, data analysis and extra features. We found, for example, that out of 28 programs, only 3 include a calibration algorithm to reduce image distortion and perspective problems that affect accuracy and can result in substantial errors when analyzing trajectories and extracting mobility or explored distance. In addition, only 4 programs can directly export in-depth tracking and analysis metrics, only 5 are suited for tracking multiple unmarked animals for more than a few seconds and only 11 have been updated in the period 2019–2021

Comprehensive Survey and Analysis of Techniques, Advancements, and Challenges in Video-Based Traffic Surveillance Systems

The challenges inherent in video surveillance are compounded by a several factors, like dynamic lighting conditions, the coordination of object matching, diverse environmental scenarios, the tracking of heterogeneous objects, and coping with fluctuations in object poses, occlusions, and motion blur. This research endeavor aims to undertake a rigorous and in-depth analysis of deep learning- oriented models utilized for object identification and tracking. Emphasizing the development of effective model design methodologies, this study intends to furnish a exhaustive and in-depth analysis of object tracking and identification models within the specific domain of video surveillance

Advances in Object and Activity Detection in Remote Sensing Imagery

The recent revolution in deep learning has enabled considerable development in the fields of object and activity detection. Visual object detection tries to find objects of target classes with precise localisation in an image and assign each object instance a corresponding class label. At the same time, activity recognition aims to determine the actions or activities of an agent or group of agents based on sensor or video observation data. It is a very important and challenging problem to detect, identify, track, and understand the behaviour of objects through images and videos taken by various cameras. Together, objects and their activity recognition in imaging data captured by remote sensing platforms is a highly dynamic and challenging research topic. During the last decade, there has been significant growth in the number of publications in the field of object and activity recognition. In particular, many researchers have proposed application domains to identify objects and their specific behaviours from air and spaceborne imagery. This Special Issue includes papers that explore novel and challenging topics for object and activity detection in remote sensing images and videos acquired by diverse platforms

Deep Learning Approach for Large-Scale, Real-Time Quantification of Green Fluorescent Protein-Labeled Biological Samples in Microreactors

Absolute quantification of biological samples entails determining expression levels in precise numerical copies, offering enhanced accuracy and superior performance for rare templates. However, existing methodologies suffer from significant limitations: flow cytometers are both costly and intricate, while fluorescence imaging relying on software tools or manual counting is time-consuming and prone to inaccuracies. In this study, we have devised a comprehensive deep-learning-enabled pipeline that enables the automated segmentation and classification of GFP (green fluorescent protein)-labeled microreactors, facilitating real-time absolute quantification. Our findings demonstrate the efficacy of this technique in accurately predicting the sizes and occupancy status of microreactors using standard laboratory fluorescence microscopes, thereby providing precise measurements of template concentrations. Notably, our approach exhibits an analysis speed of quantifying over 2,000 microreactors (across 10 images) within remarkably 2.5 seconds, and a dynamic range spanning from 56.52 to 1569.43 copies per micron-liter. Furthermore, our Deep-dGFP algorithm showcases remarkable generalization capabilities, as it can be directly applied to various GFP-labeling scenarios, including droplet-based, microwell-based, and agarose-based biological applications. To the best of our knowledge, this represents the first successful implementation of an all-in-one image analysis algorithm in droplet digital PCR (polymerase chain reaction), microwell digital PCR, droplet single-cell sequencing, agarose digital PCR, and bacterial quantification, without necessitating any transfer learning steps, modifications, or retraining procedures. We firmly believe that our Deep-dGFP technique will be readily embraced by biomedical laboratories and holds potential for further development in related clinical applications.Comment: 23 pages, 6 figures, 1 tabl

RGB-D Salient Object Detection: A Survey

Salient object detection (SOD), which simulates the human visual perception system to locate the most attractive object(s) in a scene, has been widely applied to various computer vision tasks. Now, with the advent of depth sensors, depth maps with affluent spatial information that can be beneficial in boosting the performance of SOD, can easily be captured. Although various RGB-D based SOD models with promising performance have been proposed over the past several years, an in-depth understanding of these models and challenges in this topic remains lacking. In this paper, we provide a comprehensive survey of RGB-D based SOD models from various perspectives, and review related benchmark datasets in detail. Further, considering that the light field can also provide depth maps, we review SOD models and popular benchmark datasets from this domain as well. Moreover, to investigate the SOD ability of existing models, we carry out a comprehensive evaluation, as well as attribute-based evaluation of several representative RGB-D based SOD models. Finally, we discuss several challenges and open directions of RGB-D based SOD for future research. All collected models, benchmark datasets, source code links, datasets constructed for attribute-based evaluation, and codes for evaluation will be made publicly available at https://github.com/taozh2017/RGBDSODsurveyComment: 24 pages, 12 figures. Has been accepted by Computational Visual Medi

Self-supervised foreground segmentation by sequences of images without camera motion

Sense cap mena de dubte, l’última dècada ha suposat un gran avenç pel que fa als algorismes d’aprenentatge profund. El seu impacte ha arribat a un ample rang de camps: des de visió artificial o processament de llenguatge natural, fins a medicina personalitzada o imatge biomèdica. Tot i això, la majoria de tasques que han estat solucionades per xarxes neuronals artificials depenen d’una gran quantitat de mostres ja anotades per a ser entrenades. D’aquí sorgeix la necessitat d’estratègies d’entrenament com l’aprenentatge auto-supervisat, que substitueix la necessitat de dades etiquetades per una major quantitat de mostres i un mètode que permeti extreure’n informació. En aquest treball proposem un nou algorisme per a la segmentació d’objectes en primer pla en imatges que no depèn de mostres etiquetades per al seu entrenament. En comptes d’això, aprofita la similitud que hi ha entre seqüències d’imatges amb un mateix fons per distingir aquells elements que corresponen al primer pla. A més, proposem un mètode que permet agrupar diferents regions segmentades per distingir diferents tipus d’objectes. Malgrat que aquest mètode només permet treballar amb imatges fotografiades amb una càmera fixa, ampliem l’algorisme de manera que sigui capaç d generalitzar aquests resultats a imatges mai vistes, la qual cosa ens permet avaluar-lo en conjunts de dades usats habitualment com a referència.Sin duda alguna, la última década ha supuesto un gran avance para los algoritmos de aprendizaje profundo. Su impacto ha llegado a un amplio rango de campos: desde la visión artificial o el procesado de lenguaje natural, hasta la medicina personalizada o la imagen biomédica. Aun así, la mayoría de tareas que han sido solucionadas por redes neuronales artificiales dependen de una gran cantidad de datos etiquetados para ser entrenadas. De aquí surge la necesidad de estrategias como el aprendizaje auto-supervisado, que sustituye la necesidad de muestras anotadas por una mayor cantidad de datos y un método para extraer información útil de ellos. En este trabajo, proponemos un nuevo algoritmo para la segmentación de objetos en primer plano en imágenes que no depende de muestras etiquetadas para ser entrenado. En vez de esto, aprovecha la similitud entre secuencias de imágenes con un mismo fondo para distinguir aquellos elementos que corresponden al primer plano. Además, proponemos un método que permite agrupar distintas regiones segmentadas para distinguir distintos tipos de objeto. A pesar de que el algoritmo solamente puede trabajar con imágenes fotografiadas con una misma cámara fija, ampliamos nuestra propuesta para que pueda generalizar estos resultados a imágenes nunca vistas, lo cual nos permite evaluarlo en conjuntos de datos usados comúnmente como referencia.Undeniably, last decade has proven to be a success for deep learning based algorithms. It has positively impacted a wide range of fields of knowledge, that range from computer vision or natural language processing to biomedical imaging or personalized medicine. Despite this, most of the tasks that have been solved by artificial neural networks rely on a bast amount of annotated samples, which require a lot of human work to be obtained. This is where the need of new training schemes like self-supervised learning arises, that replace labeled data with a larger amount of samples and a strategy to extract meaningful information from it. In this project we propose a novel approach for image foreground segmentation that does not rely on already segmented images to be trained. Instead it exploits the similarity between sequences of images with common backgrounds to extract representations that allow to successfully distinguish foreground regions. Then we propose a method to cluster these regions to discover groups of similar type of objects. Although this approach only works with sets of images taken with a fixed-camera, we take an extra step and suggest a method to generalize to unseen backgrounds, which allows us to test our results on established benchmarks.Outgoin

Hand gesture recognition using deep learning neural networks

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonHuman Computer Interaction (HCI) is a broad field involving different types of interactions including gestures. Gesture recognition concerns non-verbal motions used as a means of communication in HCI. A system may be utilised to identify human gestures to convey information for device control. This represents a significant field within HCI involving device interfaces and users. The aim of gesture recognition is to record gestures that are formed in a certain way and then detected by a device such as a camera. Hand gestures can be used as a form of communication for many different applications. It may be used by people who possess different disabilities, including those with hearing-impairments, speech impairments and stroke patients, to communicate and fulfil their basic needs. Various studies have previously been conducted relating to hand gestures. Some studies proposed different techniques to implement the hand gesture experiments. For image processing there are multiple tools to extract features of images, as well as Artificial Intelligence which has varied classifiers to classify different types of data. 2D and 3D hand gestures request an effective algorithm to extract images and classify various mini gestures and movements. This research discusses this issue using different algorithms. To detect 2D or 3D hand gestures, this research proposed image processing tools such as Wavelet Transforms and Empirical Mode Decomposition to extract image features. The Artificial Neural Network (ANN) classifier which used to train and classify data besides Convolutional Neural Networks (CNN). These methods were examined in terms of multiple parameters such as execution time, accuracy, sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood, negative likelihood, receiver operating characteristic, area under ROC curve and root mean square. This research discusses four original contributions in the field of hand gestures. The first contribution is an implementation of two experiments using 2D hand gesture video where ten different gestures are detected in short and long distances using an iPhone 6 Plus with 4K resolution. The experiments are performed using WT and EMD for feature extraction while ANN and CNN for classification. The second contribution comprises 3D hand gesture video experiments where twelve gestures are recorded using holoscopic imaging system camera. The third contribution pertains experimental work carried out to detect seven common hand gestures. Finally, disparity experiments were performed using the left and the right 3D hand gesture videos to discover disparities. The results of comparison show the accuracy results of CNN being 100% compared to other techniques. CNN is clearly the most appropriate method to be used in a hand gesture system.Imam Abdulrahman bin Faisal Universit

Deep learning algorithms for background subtraction and people detection

Video cameras are commonly used today in surveillance and security, autonomous driving and flying, manufacturing and healthcare. While different applications seek different types of information from the video streams, detecting changes and finding people are two key enablers for many of them. This dissertation focuses on both of these tasks: change detection, also known as background subtraction, and people detection from overhead fisheye cameras, an emerging research topic. Background subtraction has been thoroughly researched to date and the top-performing algorithms are data-driven and supervised. Crucially, during training these algorithms rely on the availability of some annotated frames from the video being tested. Instead, we propose a novel, supervised background-subtraction algorithm for unseen videos based on a fully-convolutional neural network. The input to our network consists of the current frame and two background frames captured at different time scales along with their semantic segmentation maps. In order to reduce the chance of overfitting, we introduce novel temporal and spatio-temporal data-augmentation methods. We also propose a cross-validation training/evaluation strategy for the largest change-detection dataset, CDNet-2014, that allows a fair and video-agnostic performance comparison of supervised algorithms. Overall, our algorithm achieves significant performance gains over state of the art in terms of F-measure, recall and precision. Furthermore, we develop a real-time variant of our algorithm with performance close to that of the state of the art. Owing to their large field of view, fisheye cameras mounted overhead are becoming a surveillance modality of choice for large indoor spaces. However, due to their top-down viewpoint and unique optics, standing people appear radially oriented and radially distorted in fisheye images. Therefore, traditional people detection, tracking and recognition algorithms developed for standard cameras do not perform well on fisheye images. To address this, we introduce several novel people-detection algorithms for overhead fisheye cameras. Our first two algorithms address the issue of radial body orientation by applying a rotating-window approach. This approach leverages a state-of-the-art object-detection algorithm trained on standard images and applies additional pre- and post-processing to detect radially-oriented people. Our third algorithm addresses both the radial body orientation and distortion by applying an end-to-end neural network with a novel angle-aware loss function and training on fisheye images. This algorithm outperforms the first two approaches and is two orders of magnitude faster. Finally, we introduce three spatio-temporal extensions of the end-to-end approach to deal with intermittent misses and false detections. In order to evaluate the performance of our algorithms, we collected, annotated and made publicly available four datasets composed of overhead fisheye videos. We provide a detailed analysis of our algorithms on these datasets and show that they significantly outperform the current state of the art