17,727 research outputs found
HOG, LBP and SVM based Traffic Density Estimation at Intersection
Increased amount of vehicular traffic on roads is a significant issue. High
amount of vehicular traffic creates traffic congestion, unwanted delays,
pollution, money loss, health issues, accidents, emergency vehicle passage and
traffic violations that ends up in the decline in productivity. In peak hours,
the issues become even worse. Traditional traffic management and control
systems fail to tackle this problem. Currently, the traffic lights at
intersections aren't adaptive and have fixed time delays. There's a necessity
of an optimized and sensible control system which would enhance the efficiency
of traffic flow. Smart traffic systems perform estimation of traffic density
and create the traffic lights modification consistent with the quantity of
traffic. We tend to propose an efficient way to estimate the traffic density on
intersection using image processing and machine learning techniques in real
time. The proposed methodology takes pictures of traffic at junction to
estimate the traffic density. We use Histogram of Oriented Gradients (HOG),
Local Binary Patterns (LBP) and Support Vector Machine (SVM) based approach for
traffic density estimation. The strategy is computationally inexpensive and can
run efficiently on raspberry pi board. Code is released at
https://github.com/DevashishPrasad/Smart-Traffic-Junction.Comment: paper accepted at IEEE PuneCon 201
A Computer Vision System to Localize and Classify Wastes on the Streets
Littering quantification is an important step for improving cleanliness of
cities. When human interpretation is too cumbersome or in some cases
impossible, an objective index of cleanliness could reduce the littering by
awareness actions. In this paper, we present a fully automated computer vision
application for littering quantification based on images taken from the streets
and sidewalks. We have employed a deep learning based framework to localize and
classify different types of wastes. Since there was no waste dataset available,
we built our acquisition system mounted on a vehicle. Collected images
containing different types of wastes. These images are then annotated for
training and benchmarking the developed system. Our results on real case
scenarios show accurate detection of littering on variant backgrounds
Fine-Grained Car Detection for Visual Census Estimation
Targeted socioeconomic policies require an accurate understanding of a
country's demographic makeup. To that end, the United States spends more than 1
billion dollars a year gathering census data such as race, gender, education,
occupation and unemployment rates. Compared to the traditional method of
collecting surveys across many years which is costly and labor intensive,
data-driven, machine learning driven approaches are cheaper and faster--with
the potential ability to detect trends in close to real time. In this work, we
leverage the ubiquity of Google Street View images and develop a computer
vision pipeline to predict income, per capita carbon emission, crime rates and
other city attributes from a single source of publicly available visual data.
We first detect cars in 50 million images across 200 of the largest US cities
and train a model to predict demographic attributes using the detected cars. To
facilitate our work, we have collected the largest and most challenging
fine-grained dataset reported to date consisting of over 2600 classes of cars
comprised of images from Google Street View and other web sources, classified
by car experts to account for even the most subtle of visual differences. We
use this data to construct the largest scale fine-grained detection system
reported to date. Our prediction results correlate well with ground truth
income data (r=0.82), Massachusetts department of vehicle registration, and
sources investigating crime rates, income segregation, per capita carbon
emission, and other market research. Finally, we learn interesting
relationships between cars and neighborhoods allowing us to perform the first
large scale sociological analysis of cities using computer vision techniques.Comment: AAAI 201
LIDAR-based Driving Path Generation Using Fully Convolutional Neural Networks
In this work, a novel learning-based approach has been developed to generate
driving paths by integrating LIDAR point clouds, GPS-IMU information, and
Google driving directions. The system is based on a fully convolutional neural
network that jointly learns to carry out perception and path generation from
real-world driving sequences and that is trained using automatically generated
training examples. Several combinations of input data were tested in order to
assess the performance gain provided by specific information modalities. The
fully convolutional neural network trained using all the available sensors
together with driving directions achieved the best MaxF score of 88.13% when
considering a region of interest of 60x60 meters. By considering a smaller
region of interest, the agreement between predicted paths and ground-truth
increased to 92.60%. The positive results obtained in this work indicate that
the proposed system may help fill the gap between low-level scene parsing and
behavior-reflex approaches by generating outputs that are close to vehicle
control and at the same time human-interpretable.Comment: Changed title, formerly "Simultaneous Perception and Path Generation
Using Fully Convolutional Neural Networks
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