Claims:WE CLAIM:
1) A pedestrian and cyclist detection system for the bus comprising of;
a. Detecting unprotected road-users near the bus with the help of sensors fitted in the bus;
b. transmitting a sound to warn other road-users that the bus is approaching;
c. simultaneously alerting the driver is alerted via sound and light signals inside the vehicle; and
d. activating the bus horn in case of imminent risk of an incident.
2) The pedestrian and cyclist detection system for the bus as claimed in claim 1, wherein the said system Detects unprotected road-users near the bus with the help of sensors fitted in the bus and transfers the message to the database of the website.
3) The pedestrian and cyclist detection system for the bus as claimed in claim 1, wherein the said system continuously monitors the bus’s vicinity using a camera.
, Description:FIELD OF THE INVENTION:
The present invention relates to a pedestrian and cyclist detection system .The present invention more particularly relates to a pedestrian and cyclist detection system for the bus.
BACKGROUND OF THE INVENTION:
The term pedestrian is a person who is walking or running on the street. In some communities, a person using wheelchair is also considered as pedestrians. The most challenging task for automatic video surveillance is to detect and track the suspicious pedestrian activity. For a real-time dynamic environment, the learning-based methods did not provide an appropriate solution for real-time scene analysis because it is difficult to obtain a prior knowledge about all the objects. Still, the learning-based methods are adopted due to their accuracy and robust nature.
Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems.
In recent years, various intelligent systems employing deep learning techniques have been developed. Until some years ago, the use of deep neural networks in smart applications was hindered by several shortcomings, such as poor recognition rates, slow processing of large datasets, and difficulties in the learning of multilayer networks. However, rapid progress in GPGPU (general-purpose graphics processing units) and the advancement of deep learning architectures have reignited researchers' interest in deep learning applications over the last ten years . Nowadays, deep learning is applied to practical applications in many areas, including intelligent transportation systems, autonomous cars, unmanned aerial vehicles, robots, and artificial intelligence agents .Pedestrian detection and tracking via a low-altitude unmanned aerial vehicle (UAV) is a critical component for building ITS (intelligent transportation system).
Transportation and traffic management application that exploits advanced technologies in Electricity, Electronics, Information and Communications, and Control Engineering, to improve safety, mobility and efficiency. Such intelligent systems must incorporate the capability to collect traffic data.1In current transportation systems, traffic data are mostly managed by fixed sensing nodes, such as a ground loop detector, wireless sensors, and monitoring cameras. These fixed sensors can sustain for a long time once they are installed, but they have high installation and maintenance costs. Alternatively, traffic data can be obtained by analyzing aerial images from satellites, aircraft, helicopters, airships, and UAVs. Satellite and aircraft images are expensive and challenging to reflect time and weather changes on the fly. On the other hand, airships and UAVs can provide high-definition aerial images with relatively low costs. In particular, the use of aerial images taken by UAVs to detect and trace the objects on the ground is an actively researched area.
Accidents involving buses and unprotected road-users seldom occur, but when they do the consequences may be very serious. In order to minimize the risks, it is important that drivers and anyone moving around near buses – such as at bus stops and pedestrian crossings – pay close attention to the traffic. As the proportion of electrified vehicles in urban traffic continues to grow, exhaust fumes and noise continue to disappear. With less noise comes less warning, so the company believes it to be important to eliminate any risks that might arise as vehicles in urban environments continue to operate much more quietly.
The present invention is a pedestrian detection and tracking method for buses using Internet of things (IOT).
OBJECTS OF THE INVENTION:
The main object of the present invention is to provide a pedestrian detection and tracking method for buses using Internet of things (IOT).
Another object of the present invention is to provide a system in order to minimize the risks and pay close attention to the traffic.
Other objects and benefits of the present invention will be more apparent from the following description, which is not intended to bind the scope of the present invention
SUMMARY OF THE INVENTION:
Accordingly, a system for a pedestrian detection and tracking method for buses using Internet of things (IOT) is disclosed. A pedestrian and cyclist detection system for the bus comprising of; Detecting unprotected road-users near the bus with the help of sensors fitted in the bus; transmits a sound to warn other road-users that the bus is approaching; simultaneously alerting the driver is alerted via sound and light signals inside the vehicle; and activating the bus horn in case of imminent risk of an incident.
DESCRIPTION OF THE DRAWINGS:
Fig 1 is the flowchart for a pedestrian detection and tracking method for buses using Internet of things (IOT).
DETAILED DESCRIPTION OF THE INVENTION WITH RESPECT TO DRAWINGS:
The present invention is a pedestrian detection and tracking method for buses using Internet of things (IOT). The present invention provides a system in order to minimize the risks and pay close attention to the traffic.
In one embodiment, The present invention is a pedestrian and cyclist detection system for the bus. The present invention offer a solution that simultaneously notifies both driver and unprotected road-user. The Pedestrian and Cyclist Detection System continuously monitors the bus’s vicinity using a camera. When the system detects unprotected road-users near the bus, it transmits a sound to warn other road-users that the bus is approaching. At the same time, the driver is alerted via sound and light signals inside the vehicle. If there is an imminent risk of an incident, the bus’s horn is activated.
In another embodiment, the first stage of detection: sensing (the second half of detection occurs when on-board computers process and interpret sensed images or signals). There is neither consensus among manufacturers as to which of the many available classes of sensors perform best nor any set standard for assessing sensor performance. Production AVs are likely to incorporate multisensor systems combining several overlapping technologies so the strengths of one sensor offset the weaknesses of another.
The most common classes of sensors presently used in automated driving applications include visible-light cameras (VLC), light detection and ranging (LiDAR), and radar (although infrared imaging technology is commonly used in driver assistance systems, e.g., parking assist, it has not been widely deployed in AV applications). Nearly all sensors are unique and proprietary, meaning their capabilities and limitations vary across applications. Within sensor classes, however, individual applications share some common limitations, described below.
Multiple VLCs capture images and send them to onboard computers, which compile and analyze them for pedestrians and objects. VLCs function poorly in low-light conditions, in adverse weather conditions, and on slick surfaces where glare might be a factor. LiDAR uses scanning lasers to measure distances to surfaces, producing a three-dimensional map of detailed shapes. LiDAR is capable of object detection in low/no-light conditions, but like VLC, is unreliable in adverse weather and when road surfaces are wet or reflective. LiDAR is potentially useful for medium- and long-range detection, but is typically deployed as a single unit on a vehicle's rooftop, with its “view” of the ground surrounding the vehicle obstructed by the vehicle itself, hindering detection at very close range. Radar uses radio waves to detect objects and determine distances and relative speeds. Radar handles darkness and adverse weather conditions well, but has poor resolution, making it difficult to distinguish pedestrians, especially children. Radar also cannot reliably detect stationary objects (such as pedestrians waiting to enter a roadway).
In another embodiment, A pedestrian and cyclist detection system for the bus comprising of;
a. Detecting unprotected road-users near the bus with the help of sensors fitted in the bus;
b. transmits a sound to warn other road-users that the bus is approaching;
c. simultaneously alerting the driver is alerted via sound and light signals inside the vehicle; and
d. activating the bus horn in case of imminent risk of an incident.