A Survey on Counting People with Low Level Features

The main objective of this paper is to evaluate recent development in counting people with low level features. This paper describe the various techniques of counting people with low level features, compares them with the help of evaluation performance measures which are widely used for counting. The aim of this paper is to find the best method among some prominent exiting methods

Big data analytics in high-throughput phenotyping

Doctor of PhilosophyDepartment of Computer ScienceMitchell L. NeilsenAs the global population rises, advancements in plant diversity and crop yield is necessary for resource stability and nutritional security. In the next thirty years, the global population will pass 9 billion. Genetic advancements have become inexpensive and widely available to address this issue; however, phenotypic acquisition development has stagnated. Plant breeding programs have begun to support efforts in data mining, computer vision, and graphics to alleviate the gap from genetic advancements. This dissertation creates a bridge between computer vision research and phenotyping by designing and analyzing various deep neural networks for concrete applications while presenting new and novel approaches. The significant contributions are research advancements to the current state-of-the-art in mobile high-throughput phenotyping (HTP), which promotes more efficient plant science workflow tasks. Novel tools and utilities created for automatic code generation, maintenance, and source translation are featured. Promoted tools replace boiler-plate segments and redundant tasks. Finally, this research investigates various state-of-the-art deep neural network architectures to derive methods for object identification and enumeration. Seed kernel counting is a crucial task in the plant research workflow. This dissertation explains techniques and tools for generating data to scale training. New dataset creation methodologies are debuted and aim to replace the classical approach to labeling data. Although HTP is a general topic, this research focuses on various grains and plant-seed phenotypes. Applying deep neural networks to seed kernels for classification and object detection is a relatively new topic. This research uses a novel open-source dataset that supports future architectures for detecting kernels. State-of-the-art pre-trained regional convolutional neural networks (RCNN) perform poorly on seeds. The proposed counting architectures outperform the models above by focusing on learning a labeled integer count rather than anchor points for localization. Concurrently, pre-trained models on the seed dataset, a composition of geometrically primitive-like objects, boasts improvements to evaluation metrics in comparison to the Common Object in Context (COCO) dataset. A widely accepted problem in image processing is the segmentation of foreground objects from the background. This dissertation shows that state-of-the-art regional convolutional neural networks (RCNN) perform poorly in cases where foreground objects are similar to the background. Instead, transfer learning leverages salient features and boosts performance on noisy background datasets. The accumulation of new ideas and evidence of growth for mobile computer vision surmise a bright future for data-acquisition in various fields of HTP. The results obtained provide horizons and a solid foundation for future research to stabilize and continue the growth of phenotypic acquisition and crop yield

A LITERATURE STUDY ON CROWD(PEOPLE) COUNTING WITH THE HELP OF SURVEILLANCE VIDEOS

The categories of crowd counting in video falls in two broad categories: (a) ROI counting which estimates the total number of people in some regions at certain time instance (b) LOI counting which counts people who crosses a detecting line in certain time duration. The LOI counting can be developed using feature tracking techniques where the features are either tracked into trajectories and these trajectories are clustered into object tracks or based on extracting and counting crowd blobs from a temporal slice of the video. And the ROI counting can be developed using two techniques: Detection Based and Feature Based and Pixel Regression Techniques. Detection based methods detect people individually and count them. It utilizes any of the following methods:- Background Differencing, Motion and Appearance joint segmentation, Silhouette or shape matching and Standard object recognition method. Regression approaches extract the features such as foreground pixels and interest points, and vectors are formed with those features and it uses machine learning algorithms to subside the number of pedestrians or people. Some of the common features according to recent survey are edges, wavelet coefficients, and combination of large set of features. Some of the common Regressions are Linear Regression, Neural Networks, Gaussian Process Regression and Discrete Classifiers. This paper aims at presenting a decade survey on people (crowd) counting in surveillance videos

Three Puzzles on Mathematics, Computation, and Games

In this lecture I will talk about three mathematical puzzles involving mathematics and computation that have preoccupied me over the years. The first puzzle is to understand the amazing success of the simplex algorithm for linear programming. The second puzzle is about errors made when votes are counted during elections. The third puzzle is: are quantum computers possible?Comment: ICM 2018 plenary lecture, Rio de Janeiro, 36 pages, 7 Figure

Video-based crowd counting using a multi-scale optical flow pyramid network

This paper presents a novel approach to the task of video-based crowd counting, which can be formalized as the regression problem of learning a mapping from an input image to an output crowd density map. Convolutional neural networks (CNNs) have demonstrated striking accuracy gains in a range of computer vision tasks, including crowd counting. However, the dominant focus within the crowd counting literature has been on the single-frame case or applying CNNs to videos in a frame-by-frame fashion without leveraging motion information. This paper proposes a novel architecture that exploits the spatiotemporal information captured in a video stream by combining an optical flow pyramid with an appearance-based CNN. Extensive empirical evaluation on five public datasets comparing against numerous state-of-the-art approaches demonstrates the efficacy of the proposed architecture, with our methods reporting best results on all datasets. Finally, a set of transfer learning experiments shows that, once the proposed model is trained on one dataset, it can be transferred to another using a limited number of training examples and still exhibit high accurac