We present a series of adaptations in low probability distributions scenarios to detect and track multiple moving objects of interest. We investigate the benefits of the linearization of the loss trajectory1 in training neural networks, mainly addressing the lack of auto-differentiation in MOTA2 evaluations, and observe what characteristics can support parallelism3 and differential computation and to what extent these observations contributes to our objectives. Using benchmarks from DeepMOT4 and CenterNet,5 we highlight the use of sparsemax activations by mounting a finite number of independent, asynchronous detectors to augment performance and gain from compounded accuracy.∗ Empirical results show optimistic gains when applying parallelization on low-powered, low-latency embedded systems in cases where automatic differentiation is available.
The main reasons behind the traffic jam and accidents are illegal/double parking, over-speeding, violating signal lights, construction, wrong-way driving, reckless driving, unsafe lane changing, etc. To determines the problems and the solutions, this study proposes a two-step approach. One is data collection and the other is Optimization. In the data collection part, traffic information is obtained from various traffic information units through cameras installed in traffic signals and roads. By analyzing the collected data, the systems can take the next step in the optimization part. In the collected data, it is required to detect vehicles, pedestrians, and lanes. Yolov3 method was used for vehicles and pedestrians’ detection. For lane detection, the Hough transform was used. The main goal of the research is not detecting objects but to determine the intelligent systems which can combine all the collected data and give the optimum solutions to control the traffic signals depending on the situations. The study found that sometimes the vehicles are unnecessarily waiting for the signals. If the unnecessary time could be saved through signals then it would reduce time consumption, oil consumption, and mental impatience. The result would give us opportunities to reduce accidents, pollution, money, and time. Moreover, the systems can measure the speed which helps find out rule violating vehicles. This paper also proposed a method for shortest path calculation. In addition, automatic penalty execution can be carried out through the collected data. For that number plate recognition is included in future works.
Accurate species identification of parasitic bees is needed for studies of bio-pesticide use and habitat investigation. Therefore, we use differences in the shape of the wing veins between parasitic bee species. In this paper, we build a system that can automatically recognize species based on the extracted features from wing vein images.
In this paper, we propose a method to analyze and support the performance status from the images of hands and keys obtained by installing a web camera on a head-mounted display (HMD).
Social distancing is a suggested solution by many scientists, health care providers and researchers to reduce the spread of COVID-19 in public places. Over a year ago most countries have closed their borders, put people under lockdown, and have been suspending people from work and travel. However, there are still many organizations that need to operate, especially hospitals, services industry, governments, etc. However, people cannot maintain social distancing which includes staying at least 1.5 2 meters from other people because they need to communicate with each other. As a result, this increases the infection of Covid-19. This work proposes a social distancing tracking tool in offices or indoor places. We propose a YOLOv5-based Deep Neural Network (DNN) model to automate the process of monitoring the social distancing via object detection and tracking approaches. We detect office objects of known size and use it to estimate the social distance in real-time with the bounding boxes in indoor environments.
Humans and animals have the ability to continually acquire, fine-tune, and transfer knowledge and skills throughout their lifespan. This ability, is referred to as lifelong learning. In machine learning, however, algorithms are trained on available data and re-training them on new data is a major challenge due to catastrophic forgetting. Therefore, lifelong learning remains a long-standing challenge for machine learning and neural network models. In this research we aimed to solve the continual learning catastrophic forgetting issue using image processing methods. We proposed multiple methods including alpha blending, histogram equalization and a pruner. We build a universal network, using a previously trained one as a feature extractor. After that, we implemented our proposed methods. In alpha blending method, we used a technique that separate knowledge from unknown knowledge. This method shows better results than normal concatenation technique. The other method was applying histogram equalization. In this method we used two kind of techniques; one is equalizer as pruner and the other is equalizer as enhancer. Our methods got promising results.
Since contamination of food products by insects can cause serious damage to manufacturers, it is necessary to determine the timing of contamination as a countermeasure. However, manual surveys are time consuming and expensive. In this paper, we propose a method for automatic identification of insect infiltration routes using image processing.
This paper proposes a method to estimate 3D shape of puppet head for a digital archiving. To reconstruct an inner shape of head, we use CT images. First, we divide four regions (wood, hair, paint and air) by thresholds based on manual directed regions. Subsequently, we divide these regions by a graph cut method. We also extract 3D shapes of parts in puppet head based on a graph cut method. We apply these methods to puppet heads and we confirm effectiveness of our proposed method.
Facial expressions and hand gestures are recognized as a part of human emotions especially in a feeling of fatigue signs. In computer vision research, a positioning of hand over face is one of the challenging problem caused by difficulty of the difference of skin color for hands and face. In this paper, we present a method for classifying six positions of the hand over face which is able to identify the signs of feeling fatigue for the visual display terminal (VDT) workers. We apply a deep learning method in order to compare with the methods used the face and skin colors detection, processing the edge detection and feature extraction algorithms. In addition, GoogleNet is used for training a data set made in the simulated VDT workers environment. The data set includes 1,440 images, the participants from several countries, Egypt, Japan, Bangladesh, Mongolia and Rwanda to cover a wide range of skin tones. The data set is categorized by six groups of the positions of hand over face. These groups consist of hands on a forehead, eyes, nose, mouth, right and left face. The experiments were performed using MATLAB to implement our proposed method. The system achieved average recognition ratio 99.3 % in all hand over face gestures.
By carrying out marketing research, the managers of large-sized department stores or small convenience stores obtain the information such as ratio of men and women of visitors and an age group, and improve their management plan. However, these works are carried out in the manual operations, and it becomes a big burden to small stores. In this paper, the authors propose a method of men and women discrimination by extracting difference of the facial expression change from color facial images. Now, there are a lot of methods of the automatic recognition of the individual using a motion facial image or a still facial image in the field of image processing. However, it is very difficult to discriminate gender under the influence of the hairstyle and clothes, etc. Therefore, we propose the method which is not affected by personality such as size and position of facial parts by paying attention to a change of an expression. In this method, it is necessary to obtain two facial images with an expression and an expressionless. First, a region of facial surface and the regions of facial parts such as eyes, nose, and mouth are extracted in the facial image with color information of hue and saturation in HSV color system and emphasized edge information. Next, the features are extracted by calculating the rate
of the change of each facial part generated by an expression change. In the last step, the values of those features are compared between the input data and the database, and the gender is discriminated. In this paper, it experimented for the laughing expression and smile expression, and good results were provided for discriminating gender.
In this paper, we propose a method of surveillance of the plant growth using the camera image. This method is able to observe the condition of raising the plant in the greenhouse. The plate which is known as HORIBA is prepared for extracting harmful insect. The image of HORIBA is obtained by the camera and used for processing. The resolution of the image is 1280×960. In first process, region of the harmful insect (fly) is extracted from HORIBA by using color information. In next process the template matching is performed to examine the correlation of shape in four different angles. 16 kinds of results are obtained by four different templates. The sum logical of the results is calculated for estimation. In addition, the experimental results are shown in this paper.
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