Object Counting Research Articles

Exploring adults' awareness of and suggestions for early childhood numerical activities.

This study focuses on adults who are neither preschool teachers nor professional caregivers and investigates their beliefs regarding the importance of engaging young children with numerical activities. It also examines the types of numerical activities adults report having observed children engaging with, as well as the types of activities they propose as a way for promoting counting, enumerating, recognizing number symbols, and number composition and decomposition. Findings showed that participants believed to a great extent that engaging young children with numerical activities is important. Most reported that they had observed children engaging with at least some numerical activity. In general, participants relayed more activities and more detailed activities when suggesting activities for each competency, than they did when reporting observed activities. Findings also suggested a need to enhance adults’ knowledge regarding the necessity to promote verbal counting, separate from object counting, as well as to increase adults’ awareness of number composition and decomposition. For mathematics educators wishing to plan workshops for adults, this study offers a method for investigating adults’ knowledge of early numerical activities, as well as a starting point with which to plan appropriate workshops.

Warehouse Management Models Using Artificial Intelligence Technology with Application at Receiving Stage – A Review

This paper reviewed recent literature on inventory management technologies and Artificial Intelligence (AI) applications. The classical Artificial Neural Network (ANN) models and computer vision technology applications for object classification were reviewed in particularly. The challenges of AI technologies in industrial warehouse management, particularly the ANN for solving object classification and counting are discussed. Some researchers reported the use of face recognition, moving vehicle classification and counting, which are easy to recognise objects on the floor or the ground. Other researchers explored the object counting technologies which are used to identify the visible objects on the ground or in images. Although several studies focused on industrial component identification and counting problems, a study on the warehouse receiving stage remains a blank canvas. This paper reviews and analyses current industrial warehouse management developments around AI applications in this field, which may provide a reference for future researchers and end-users for the best modelling approach to this specific problem at the warehouse receiving stage.

Object Counting using Deep Learning

In this paper, we consider the Problem of Object counting in deep learning. It is frequently carried out in different place of industries, school and colleges, traffic places among others. Object counting is major for quantitative analyses that rely on evaluation on certain objects. In this work, we propose a deep learning to find this challenge. Unfortunately, Object counting is most commonly a manual task and can be time intensive. As a result, we manage both to increase the accuracy count and decrease the processing time. A Deep Learning based system can be used for real time applications.

VAGADRONE: Intelligent and Fully Automatic Drone Based on Raspberry Pi and Android

Multirotor drones are widely used currently in several areas of life. Their suitable size and the tasks that they can perform are their main advantages. However, to the best of our knowledge, they must be controlled via remote control to fly from one point to another, and they can only be used for a specific mission (tracking, searching, computing, and so on). In this paper, we intend to present an autonomous UAV based on Raspberry Pi and Android. Android offers a wide range of applications for direct use by the UAV depending on the context of the assigned mission. The applications cover a large number of areas such as object identification, facial recognition, and counting objects such as panels, people, and so on. In addition, the proposed UAV calculates optimal trajectories, provides autonomous navigation without external control, detects obstacles, and ensures live streaming during the mission. Experiments are carried out to test the above-mentioned criteria.

3D-CNN based UAV hyperspectral imagery for grassland degradation indicator ground object classification research

The identification and counting of grassland degradation indicator ground objects is an important component of grassland ecological monitoring. These steps are also an important basis for developing ecological restoration and management programs for degraded grasslands. Compared with a traditional human survey, the use of remote sensing images can not only achieve dynamic monitoring of a large area, but also improve the efficiency. Recently, most studies regarding ground object classification based on remote sensing images address the development and optimization of classification models for features in several widely used datasets. For the remote sensing of desertified grasslands, remote sensing images with high spatial resolutions are used for studies on small and sparse features in degraded grasslands. The spatial resolution of the above mentioned datasets yields difficulties when attempting to classify small and sparse indicator features for desertified grasslands because generalization becomes limited. Therefore, establishing a lightweight classification model suitable for degraded grassland features with high spatial resolution is important. In this study, a low altitude unmanned aerial vehicle (UAV) hyperspectral remote sensing platform was constructed to collect high spatial resolution remote sensing images of degraded grasslands. The GDIF-3D-CNN classification model was used to classify the pure pixels and all pixels datasets, whose accuracy and efficiency were further improved by optimizing the eight parameters of the model. This study explores the remote sensing ground object classification of thin small plants and a large number of mixed pixels, realizing high precision classification among desertification degradation indicating plant populations of a species, and provides key quantitative data for grassland degradation research.

A Conceptual Artificial Neural Network Model in Warehouse Receiving Management

The purpose of this research is to explore a suitable Artificial Neural Network (ANN) method applying to warehouse receiving management. A conceptual ANN model is proposed to perform identification and counting of components. The proposed model consists of a standard image library, an ANN system to present objects for identification from the real-time images and to count the number of objects in the image. The authors adopted four basic mechanical design shapes as the attributes of images for shape analysis and pre-defined features; the joint probability from Bayes theorem and image pixel values for object counting is applied in this research. Compared to other ANNs, the proposed conceptual model is straightforward to perform classification and counting. The model is tested by employing a mini image dataset which is industrial enterprise relevant. The initial result shows that the proposed model has achieved an accuracy rate of 80% in classification and a 97% accuracy rate in counting. The development of the model is associated with a few challenges, including exploring algorithms to enhance the accuracy rate for component identification and testing the model in a larger dataset.

Fully Automatic Multi-Object Articulated Motion Tracking

Fully automatic tracking of articulated motion in real-time with a monocular RGB camera is a challenging problem which is essential for many virtual reality (VR) and human-computer interaction applications. In this paper, we present an algorithm for multiple articulated objects tracking based on monocular RGB image sequence. Our algorithm can be directly employed in practical applications as it is fully automatic, real-time, and temporally stable. It consists of the following stages: dynamic objects counting, objects specific 3D skeletons generation, initial 3D poses estimation, and 3D skeleton fitting which fits each 3D skeleton to the corresponding 2D body-parts locations. In the skeleton fitting stage, the 3D pose of every object is estimated by maximizing an objective function that combines a skeleton fitting term with motion and pose priors. To illustrate the importance of our algorithm for practical applications, we present competitive results for real-time tracking of multiple humans. Our algorithm detects objects that enter or leave the scene, and dynamically generates or deletes their 3D skeletons. This makes our monocular RGB method optimal for real-time applications. We show that our algorithm is applicable for tracking multiple objects in outdoor scenes, community videos, and low-quality videos captured with mobile-phone cameras. Keywords: Multi-object motion tracking, Articulated motion capture, Deep learning, Anthropometric data, 3D pose estimation. DOI: 10.7176/CEIS/12-1-01 Publication date: March 31 st 2021

Commentary on a special issue: Davydov\u2019s approach in the XXI century

The articles in this special issue, collectively, provide overwhelming evidence that a curriculum which has as its primary basis the counting of discrete objects (and hence which introduces numbers as discrete) is not an effective or rational organisation. In this commentary, I discuss the contribution of each article to an understanding of Davydov’s ontology and epistemology, issues around the transposition of a pedagogy or curriculum from one country to another, early algebra and proportional or multiplicative reasoning. From Davydov’s own research and the writing in these articles, we know children are able to understand abstract structures from an early age. The thinking in this special issue provides tools to investigate and question any context in which such understanding is not routinely taking hold.

Evaluation of the Effect of Cannabidiol on Naturally Occurring Osteoarthritis-Associated Pain: A Pilot Study in Dogs

The objective of this study was to provide preliminary data describing the safety and effect of cannabidiol (CBD) for symptom relief of canine osteoarthritis-associated pain in a clinical setting using objective outcome measures. Twenty-three client-owned dogs with naturally occurring osteoarthritis of appendicular joints completed this prospective, double-blinded, crossover, placebo-controlled study. Baseline data were acquired for 4 wk, followed by random allocation to either placebo or CBD treatment for 6 wk, followed by 6 wk with the opposite treatment. Outcome measures included objective gait analysis, activity counts (via accelerometry) and clinical metrology instruments. There were no differences noted between groups at any time point for any of the recorded outcome measures. Adverse events associated with CBD administration included elevation in liver enzymes (n = 14) and vomiting (n = 2).

Dilated-Scale-Aware Category-Attention ConvNet for Multi-Class Object Counting

Object counting aims to estimate the number of objects in images. The leading counting approaches focus on the single category counting task and achieve impressive performance. Note that there are multiple categories of objects in real scenes. Multi-class object counting expands the scope of application of object counting task. The multi-target detection task can achieve multi-class object counting in some scenarios. However, it requires the dataset annotated with bounding boxes. Compared with the point annotations in mainstream object counting issues, the coordinate box-level annotations are more difficult to obtain. In this paper, we propose a simple yet efficient counting network based on point-level annotations. Specifically, we first change the traditional output channel from one to the number of categories to achieve multiclass counting. Since all categories of objects use the same feature extractor in our proposed framework, their features will interfere mutually in the shared feature space. We further design a multi-mask structure to suppress harmful interaction among objects. Extensive experiments on the challenging benchmarks illustrate that the proposed method achieves state-of-the-art counting performance.

WITHDRAWN: People detection and counting using YOLOv3 and SSD models

Abstract Object detection has become a crucial task for the various applications used in the real world such as surveillance, security, and automated vehicle system. The counting of the numbers of peoples at any junction also having various applications to provide integrity to any task. To count the number of peoples at any junction, we have various methods. Among the present methods, we analyzed the two algorithms that are You Only Look Once (YOLOv3) and Single Shot multi-box Detector (SSD). Two tasks were performed independently; one is for object detection by using the image dataset, and the other one is the counting of objects by using the video dataset. In this research, these two methods are analyzed for counting as well as detection efficiency, and comparison is presented. The results have shown that the precision, recall, and F1 measure achieved for SSD is higher than YOLOV3 v3.

Real-Time Weakly Supervised Object Detection Using Center-of-Features Localization

We propose a high-speed convolutional neural network approach for weakly supervised localization (WSL) and weakly supervised object detection (WSOD). The proposed method, called center-of-features localization (COFL), performs localization of objects in a visual scene by combining both multi-label classification and regression for the number of instances of each class object. A modified Xception network architecture is used as the main feature extractor, and a classification-plus-regression loss function is used to perform the detection task. The method does not require bounding box annotations but only image labels and counts of the objects of each class in the image. This combination can produce a clear localization of objects in the scene through a masking technique between class activation maps (CAMs) and regression activation maps (RAMs). The proposed method was trained and tested on the PASCAL VOC2007 and VOC2012 datasets; it attained a mean average precision (mAP) of 47.0% and a correct localization CorLoc of 64.1% on PASCAL VOC2007 and a mAP of 42.3% and a CorLoc of 65.5% on PASCAL VOC2012 while performing object detection at a speed of ~50 fps. These results demonstrate that the network can perform object detection accurately in real-time using only image labels and object counts, which are inexpensive to annotate compared with the bounding box annotations typically employed in fully supervised object detection methods. The network far outperforms other weakly supervised methods and some fully supervised methods in terms of processing time while achieving fair accuracy.

A Revisit Histogram of Oriented Descriptor for Facial Color Image Classification Based on Fusion of Color Information

Histogram of Oriented Gradient (HOG) is a robust descriptor which is widely used in many real‐life applications, including human detection, face recognition, object counting, and video surveillance. In order to extract HOG descriptor from color images whose information is three times more than the grayscale images, researchers currently apply the maximum magnitude selection method. This method makes the information of the resulted image is reduced by selecting the maximum magnitudes. However, after we extract HOG using the unselected magnitudes of the maximum magnitude selection method, we observe that the performance is better than using the maximum magnitudes in several cases. Therefore, in this paper, we propose a novel approach for extracting HOG from color images such as Color Component Selection and Color Component Fusion. We also propose the extended kernels in order to improve the performance of HOG. With our new approaches in the color component analysis, the experimental results of several facial benchmark datasets are enhanced with the increment from 3 to 10% of accuracy. Specifically, a 95.92% of precision is achieved on the Face AR database and 75% on the Georgia Face database. The results are better more than 10 times compared with the original HOG approach.

Decoupled Two-Stage Crowd Counting and Beyond.

One of appealing approaches to counting dense objects, such as crowd, is density map estimation. Density maps, however, present ambiguous appearance cues in congested scenes, rendering infeasibility in identifying individuals and difficulties in diagnosing errors. Inspired by an observation that counting can be interpreted as a two-stage process, i.e., identifying possible object regions and counting exact object numbers, we introduce a probabilistic intermediate representation termed the probability map that depicts the probability of each pixel being an object. This representation allows us to decouple counting into probability map regression (PMR) and count map regression (CMR). We therefore propose a novel decoupled two-stage counting (D2C) framework that sequentially regresses the probability map and learns a counter conditioned on the probability map. Given the probability map and the count map, a peak point detection algorithm is derived to localize each object with a point under the guidance of local counts. An advantage of D2C is that the counter can be learned reliably with additional synthesized probability maps. This addresses important data deficiency and sample imbalanced problems in counting. Our framework also enables easy diagnoses and analyses of error patterns. For instance, we find that, the counter per se is sufficiently accurate, while the bottleneck appears to be PMR. We further instantiate a network D2CNet in our framework and report state-of-the-art counting and localization performance across 6 crowd counting benchmarks. Since the probability map is a representation independent of visual appearance, D2CNet also exhibits remarkable cross-dataset transferability. Code and pretrained models are made available at: https://git.io/d2cnet.

No Association Between the Home Math Environment and Numerical and Patterning Skills in a Large and Diverse Sample of 5- to 6-year-olds.

Selecting a large and diverse sample of 5–6-year-old preschool children (179 boys and 174 girls; Mage = 70.03 months, SDage = 3.43), we aimed to extend previous findings on variability in children’s home math environment (i.e., home math activities, parental expectations, and attitudes) and its association with children’s mathematical skills. We operationalized mathematics in a broader way than in previous studies, by considering not only children’s numerical skills but also their patterning skills as integral components of early mathematical development. We investigated the effects of children’s gender and socioeconomic status (SES) on their home math environment, examined the associations between children’s home math environment and their mathematical skills, and verified whether these associations were moderated by children’s gender and/or SES. Parents of 353 children completed a home math environment questionnaire and all children completed measures of their numerical (e.g., object counting) and patterning skills (e.g., extending repeating patterns). Results indicated no effect of children’s gender on their home math environment. There was no effect of SES on the performed home math activities, but small SES differences existed in parents’ math-related expectations and their attitudes. We found no evidence for associations between children’s home math environment and their mathematical skills. Furthermore, there were no moderating effects of gender or SES on these associations. One explanation for these findings might relate to the characteristics of the general preschool system in the country of the present study (Belgium). Future studies should consider the effect of the preschool learning environment because it might explain differences between studies and countries with regard to the home math environment and its association with mathematical skills.

ArborX

Searching for geometric objects that are close in space is a fundamental component of many applications. The performance of search algorithms comes to the forefront as the size of a problem increases both in terms of total object count as well as in the total number of search queries performed. Scientific applications requiring modern leadership-class supercomputers also pose an additional requirement of performance portability, i.e., being able to efficiently utilize a variety of hardware architectures. In this article, we introduce a new open-source C++ search library, ArborX, which we have designed for modern supercomputing architectures. We examine scalable search algorithms with a focus on performance, including a highly efficient parallel bounding volume hierarchy implementation, and propose a flexible interface making it easy to integrate with existing applications. We demonstrate the performance portability of ArborX on multi-core CPUs and GPUs and compare it to the state-of-the-art libraries such as Boost.Geometry.Index and nanoflann.

TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery.

Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

RSVQA: Visual Question Answering for Remote Sensing Data

This paper introduces the task of visual question answering for remote sensing data (RSVQA). Remote sensing images contain a wealth of information which can be useful for a wide range of tasks including land cover classification, object counting or detection. However, most of the available methodologies are task-specific, thus inhibiting generic and easy access to the information contained in remote sensing data. As a consequence, accurate remote sensing product generation still requires expert knowledge. With RSVQA, we propose a system to extract information from remote sensing data that is accessible to every user: we use questions formulated in natural language and use them to interact with the images. With the system, images can be queried to obtain high level information specific to the image content or relational dependencies between objects visible in the images. Using an automatic method introduced in this article, we built two datasets (using low and high resolution data) of image/question/answer triplets. The information required to build the questions and answers is queried from OpenStreetMap (OSM). The datasets can be used to train (when using supervised methods) and evaluate models to solve the RSVQA task. We report the results obtained by applying a model based on Convolutional Neural Networks (CNNs) for the visual part and on a Recurrent Neural Network (RNN) for the natural language part to this task. The model is trained on the two datasets, yielding promising results in both cases.

Count Estimation With a Low-Accuracy Machine Learning Model

Many Internet-of-Things (IoT) systems use machine learning techniques, such as deep neural networks. IoT systems can predict attributes, such as age, sex, car speed, human walking speed, and types of animals, using machine learning techniques. Although the functionality of machine learning is undeniable, the prediction accuracy is not always high. When a machine learning model is used to recognize several objects in an object counting system, the estimated count will have a significant error because of the accumulation of the recognition error of each object. In this study, a count estimation method that uses a confusion matrix generated in the training phase was proposed. The proposed method consists of an iterative Bayesian technique with the confusion matrix for count estimation and mitigating over-iterations technique for reducing estimated errors. The proposed method can be used even for a low-accuracy machine learning model. Experiments with synthetic and real data sets were conducted to demonstrate the functionality of the proposed method. The estimation errors of the proposed method were reduced by 64.3% in average compared to the baseline method in the experiments.

Supporting Learning Trajectories for the Development of Number Concept: Digital Games

The aim of this study is to examine the games in ABCya (https://www.abcya.com/) based on the learning trajectories for the development of number concept. To achieve this aim in the study it was examined which ABC educational game application support the learning trajectory and the development level related to number concept of games. This research was designed in qualitative research methodology based on content analysis method. The ABCya application, which includes a dynamic structure, is suitable for manipulation and includes digital game features with mathematical content which make it appropriate to support the mathematical development of pre-school students. It is seen that the games mostly support the trajectories of the “recognition of number and subitizing” and “verbal and object counting” for the children aged between 4-6. For the trajectory of the “comparing, ordering and estimating numbers” there are games supporting the perceptual and counting comparison and ordinal numbers, but games supporting the estimation skills are limited. It is very important to introduce the digital platforms that include educational game activities to both teachers and families and to inform them about their contribution to academic development of the children.