3 research outputs found
TrackNet: Simultaneous Object Detection and Tracking and Its Application in Traffic Video Analysis
Object detection and object tracking are usually treated as two separate
processes. Significant progress has been made for object detection in 2D images
using deep learning networks. The usual tracking-by-detection pipeline for
object tracking requires that the object is successfully detected in the first
frame and all subsequent frames, and tracking is done by associating detection
results. Performing object detection and object tracking through a single
network remains a challenging open question. We propose a novel network
structure named trackNet that can directly detect a 3D tube enclosing a moving
object in a video segment by extending the faster R-CNN framework. A Tube
Proposal Network (TPN) inside the trackNet is proposed to predict the
objectness of each candidate tube and location parameters specifying the
bounding tube. The proposed framework is applicable for detecting and tracking
any object and in this paper, we focus on its application for traffic video
analysis. The proposed model is trained and tested on UA-DETRAC, a large
traffic video dataset available for multi-vehicle detection and tracking, and
obtained very promising results
Patterns of Urban Foot Traffic Dynamics
Using publicly available traffic camera data in New York City, we quantify
time-dependent patterns in aggregate pedestrian foot traffic. These patterns
exhibit repeatable diurnal behaviors that differ for weekdays and weekends but
are broadly consistent across neighborhoods in the borough of Manhattan.
Weekday patterns contain a characteristic 3-peak structure with increased foot
traffic around 9:00am, 12:00-1:00pm, and 5:00pm aligned with the "9-to-5" work
day in which pedestrians are on the street during their morning commute, during
lunch hour, and then during their evening commute. Weekend days do not show a
peaked structure, but rather increase steadily until sunset. Our study period
of June 28, 2017 to September 11, 2017 contains two holidays, the 4th of July
and Labor Day, and their foot traffic patterns are quantitatively similar to
weekend days despite the fact that they fell on weekdays. Projecting all days
in our study period onto the weekday/weekend phase space (by regressing against
the average weekday and weekend day) we find that Friday foot traffic can be
represented as a mixture of both the 3-peak weekday structure and non-peaked
weekend structure. We also show that anomalies in the foot traffic patterns can
be used for detection of events and network-level disruptions. Finally, we show
that clustering of foot traffic time series generates associations between
cameras that are spatially aligned with Manhattan neighborhood boundaries
indicating that foot traffic dynamics encode information about neighborhood
character.Comment: 16 page
Background Subtraction in Real Applications: Challenges, Current Models and Future Directions
Computer vision applications based on videos often require the detection of
moving objects in their first step. Background subtraction is then applied in
order to separate the background and the foreground. In literature, background
subtraction is surely among the most investigated field in computer vision
providing a big amount of publications. Most of them concern the application of
mathematical and machine learning models to be more robust to the challenges
met in videos. However, the ultimate goal is that the background subtraction
methods developed in research could be employed in real applications like
traffic surveillance. But looking at the literature, we can remark that there
is often a gap between the current methods used in real applications and the
current methods in fundamental research. In addition, the videos evaluated in
large-scale datasets are not exhaustive in the way that they only covered a
part of the complete spectrum of the challenges met in real applications. In
this context, we attempt to provide the most exhaustive survey as possible on
real applications that used background subtraction in order to identify the
real challenges met in practice, the current used background models and to
provide future directions. Thus, challenges are investigated in terms of
camera, foreground objects and environments. In addition, we identify the
background models that are effectively used in these applications in order to
find potential usable recent background models in terms of robustness, time and
memory requirements.Comment: Submitted to Computer Science Revie