Machine Learning Approach to Detect Drowsiness on Behavioral Parameters

Object detection has received a lot of research attention in recent years because of its tight association with video analysis and picture interpretation. Face detection, vehicle detection, pedestrian counting, web photos, security systems, and self-driving automobiles are all examples of object detection. With little conscious thought, the human visual system can accomplish complicated tasks such as distinguishing multiple objects and detecting impediments. Thanks to the availability of large amounts of data, faster GPUs, and improved algorithms, we can now quickly train computers to detect and classify many elements inside a picture with high accuracy. Our project is focused on building a single-access platform for various object detection tasks. A user-interface where the user is asked for the relevant inputs and an output based on this is generated automatically by the system. Also, accuracy and precision measures are also displayed so that the user is wary of their liability extent on the generated results.

Exploring Walking Behavior in the Streets of New York City Using Hourly Pedestrian Count Data

This paper explores hourly automated pedestrian count data of seven locations in New York City to understand pedestrian walking patterns in cities. Due to practical limitations, such patterns have been studied conceptually; few researchers have explored walking as a continuous, long-term activity. Adopting an automated pedestrian counting method, we documented and observed people walking on city streets and found that unique pedestrian traffic patterns reflect land use, development intensity, and neighborhood characteristics. We observed a threshold of thermal comfort in outdoor activities. People tend to seek shade and avoid solar radiation stronger than 1248 Wh/m2 at an average air temperature of 25 °C. Automated collection of detailed pedestrian count data provides a new opportunity for urban designers and transportation planners to understand how people walk and to improve our cities to be less dependent on the automobile.

Crowd Counting with Semantic Scene Segmentation in Helicopter Footage

Continually improving crowd counting neural networks have been developed in recent years. The accuracy of these networks has reached such high levels that further improvement is becoming very difficult. However, this high accuracy lacks deeper semantic information, such as social roles (e.g., student, company worker, or police officer) or location-based roles (e.g., pedestrian, tenant, or construction worker). Some of these can be learned from the same set of features as the human nature of an entity, whereas others require wider contextual information from the human surroundings. The primary end-goal of developing recognition software is to involve them in autonomous decision-making systems. Therefore, it must be foolproof, which is, it must have good semantic understanding of the input. In this study, we focus on counting pedestrians in helicopter footage and introduce a dataset created from helicopter videos for this purpose. We use semantic segmentation to extract the required additional contextual information from the surroundings of an entity. We demonstrate that it is possible to increase the pedestrian counting accuracy in this manner. Furthermore, we show that crowd counting and semantic segmentation can be simultaneously achieved, with comparable or even improved accuracy, by using the same crowd counting neural network for both tasks through hard parameter sharing. The presented method is generic and it can be applied to arbitrary crowd density estimation methods. A link to the dataset is available at the end of the paper.

How Attractive for Walking Are the Main Streets of a Shrinking City?

This research assesses the way main streets are perceived and used by pedestrians in an industrial, Central-European city—Ostrava in Czechia. The city has recently experienced shrinkage and changing patterns of socio-economic exchange, reason why this research is timely and needed in view of city center regeneration. Four main streets have been purposefully selected for this study. The research methods include questionnaires with street users (n = 297), direct observations of human activities and pedestrian counting. A link between business types and the way the street is experienced emerged. Results also indicate that vacant and unproperly managed spaces negatively affect the desire to walk on main streets. Furthermore, pedestrian volumes coupled with the amount of static activities determined several benchmark conditions for lively street segments. This research provides recommendations for policy-making and design and planning practice for regeneration of industrial city centers undergoing commercial and spatial transformation.