Computer Vision and Internet of Things Application to Enhance \nPedestrian Safety
Bibliographic record
Abstract
With the increasing population, the issue of pedestrian safety is currently of major concern in \nmost cities of the world. Pedestrian safety is concerned with ensuring the well-being of pedestrians \nand reducing the potential risk areas as well as implementing measures to reduce accidents. The \naim of this study is to propose a computer vision and cloud-based solution that enhances pedestrian \nsafety by collecting, visualizing and analyzing pedestrian and vehicular data across different \nintersections in the city of Montreal. In the past, the rate of accidents in the City of Montreal \ninvolving pedestrians has been quite high, therefore a method to solve this problem has led to this \nstudy. \nAbout 200,000 images were collected across 43 intersections in the city of Montreal from the \nTraffic cameras – Ville de Montreal website. The data was collected from March 8, 2020, up until \nMarch 22, 2020 and then from May 1st, 2020 to 11th May 2020. An object detection and \nclassification model using Faster RCNN algorithm to identify pedestrian and vehicles at the \nintersection was implemented. Further, this model was used to obtain a dataset showing the \nnumber of pedestrians and vehicles at the intersections. The information obtained from this data \nset was used for visualization and in-depth analysis of the pedestrian and vehicle data in order to \nderive patterns of peak and non-peak hours and high-risk intersections. \nIV \nFurthermore, zero inflation poisson distribution model was implemented on our dataset to \ndisplay the timings and intersections which had zero pedestrian counts for long hours of the day \nas compared to the vehicle count. A heat map was generated to visualize the dataset and to assist \ndata viewers to identify which areas should get most attention. \nFinally, we created a prototype solution that mimicked the traffic control system by utilizing \nLEDs and microcontrollers (IoT device), cloud services, publish/subscribe model, and object \ndetection. To implement this prototype, the data obtained through the object detection model was \nsent onto the cloud (Cloud MQTT), from where it was used to control the programmed \nmicrocontrollers (IoT devices) present at the different intersections based on the vehicle and \npedestrian counts. The system managed to show excellent accuracy for detection of vehicles and \npedestrians on the dataset, and the delay experienced in controlling the microcontroller was also \nnegligible, thus making our system effective and reliable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".