4 research outputs found
Accurate pedestrian localization in overhead depth images via Height-Augmented HOG
We tackle the challenge of reliably and automatically localizing pedestrians in real-life conditions through overhead depth imaging at unprecedented high-density conditions. Leveraging upon a combination of Histogram of Oriented Gradients-like feature descriptors, neural networks, data augmentation and custom data annotation strategies, this work contributes a robust and scalable machine learning-based localization algorithm, which delivers near-human localization performance in real-time, even with local pedestrian density of about 3 ped/m2, a case in which most stateof- the art algorithms degrade significantly in performance
Pedestrian orientation dynamics from high-fidelity measurements
We investigate in real-life conditions and with very high accuracy the
dynamics of body rotation, or yawing, of walking pedestrians - an highly
complex task due to the wide variety in shapes, postures and walking gestures.
We propose a novel measurement method based on a deep neural architecture that
we train on the basis of generic physical properties of the motion of
pedestrians. Specifically, we leverage on the strong statistical correlation
between individual velocity and body orientation: the velocity direction is
typically orthogonal with respect to the shoulder line. We make the reasonable
assumption that this approximation, although instantaneously slightly
imperfect, is correct on average. This enables us to use velocity data as
training labels for a highly-accurate point-estimator of individual
orientation, that we can train with no dedicated annotation labor. We discuss
the measurement accuracy and show the error scaling, both on synthetic and
real-life data: we show that our method is capable of estimating orientation
with an error as low as 7.5 degrees. This tool opens up new possibilities in
the studies of human crowd dynamics where orientation is key. By analyzing the
dynamics of body rotation in real-life conditions, we show that the
instantaneous velocity direction can be described by the combination of
orientation and a random delay, where randomness is provided by an
Ornstein-Uhlenbeck process centered on an average delay of 100ms. Quantifying
these dynamics could have only been possible thanks to a tool as precise as
that proposed
Weakly supervised training of deep convolutional neural networks for overhead pedestrian localization in depth fields
Overhead depth map measurements capture sufficient amount of information to enable human experts to track pedestrians accurately. However, fully automating this process using image analysis algorithms can be challenging. Even though hand-crafted image analysis algorithms are successful in many common cases, they fail frequently when there are complex interactions of multiple objects in the image. Many of the assumptions underpinning the hand-crafted solutions do not hold in these cases and the multitude of exceptions are hard to model precisely. Deep Learning (DL) algorithms, on the other hand, do not require hand crafted solutions and are the current state-of-the-art in object localization in images. However, they require exceeding amount of annotations to produce successful models. In the case of object localization, these annotations are difficult and time consuming to produce. In this work we present an approach for developing pedestrian localization models using DL algorithms with efficient weak supervision from an expert. We circumvent the need for annotation of large corpus of data by annotating only small amount of patches and relying on synthetic data augmentation as a vehicle for injecting expert knowledge in the model training. This approach of weak supervision through expert selection of representative patches, suitable transformations and synthetic data augmentations enables us to successfully develop DL models for pedestrian localization efficiently