People detection in fish-eye top-views

Is the detection of people in top views any easier than from the much researched canonical fronto-parallel views (e.g. Caltech and INRIA pedestrian datasets)? We show that in both cases people appearance variability and false positives in the background limit performance. Additionally, we demonstrate that the use of fish-eye lenses further complicates the top-view people detection, since the person viewpoint ranges from nearly-frontal, at the periphery of the image, to perfect top-views, in the image center, where only the head and shoulder top profiles are visible. We contribute a new top-view fish-eye benchmark, we experiment with a state-of-the-art person detector (ACF) and evaluate approaches which balance less variability of appearance (grid of classifiers) with the available amount of data for training. Our results indicate the importance of data abundance over the model complexity and additionally stress the importance of an exact geometric understanding of the problem, which we also contribute here

Verfahren zum gemeinsamen Detektieren, Verfolgen und Klassifizieren von Objekten

tracking system for tracking objects within a field of view is proposed. The field of view includes a first zone (A1) and an adjacent zone (A0) of interest, wherein at least two gates (G1, G2) are associated with respective sides of the first zone (A1) within the field of view. The first camera (C1) is adapted to detect when an object crosses one of the at least two gates and track the object throughout the first zone (A1) and the zone (A0) of interest; wherein the tracking system is adapted to generate a first event message (ES1) in response to the object being tracked from one of the gates into the zone (A0) of interest and subsequently leaving the first zone through a dedicated gate (G1) of the at least two gates

Training method and detection method for object recognition

The present invention relates to the technical field of object recognition. A training method for object recognition from top-view images uses a step of labelling at least one training object from at least one training image using a pre-defined labelling scheme. A detection method for object recognition uses a step of applying a test window on a test image. An object recognition method comprises the training method and the detection method. A surveillance system performs the detection method. The present invention is particularly useful for object recognition in optic-distorted videos based on a machine training method. The invention is further particularly useful for person detection from top-view visible imagery and surveillance and presence monitoring in a region of interest (ROI)

Tracking system, arrangement and method for tracking objects

A tracking system for tracking objects within a field of view is disclosed. The field of view may include a first zone and an adjacent zone of interest where at least two gates are associated with respective sides of the first zone within the field of view. The first camera is configured to detect when an object crosses one of the at least two gates and track the object throughout the first zone and the zone of interest. The tracking system is configured to generate a first event message in response to the object being tracked from one of the gates into the zone of interest and subsequently leaving the first zone through a dedicated gate of the at least two gates

Method for common detecting, tracking and classifying of objects

A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression

DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture Model

International audienceIn this paper we address the problem of how to robustly train a Con-vNet for regression, or deep robust regression. Traditionally, deep regression employ the L2 loss function, known to be sensitive to outliers, i.e. samples that either lie at an abnormal distance away from the majority of the training samples, or that correspond to wrongly annotated targets. This means that, during back-propagation, outliers may bias the training process due to the high magnitude of their gradient. In this paper, we propose DeepGUM: a deep regression model that is robust to outliers thanks to the use of a Gaussian-uniform mixture model. We derive an optimization algorithm that alternates between the unsupervised detection of outliers using expectation-maximization, and the supervised training with cleaned samples using stochastic gradient descent. DeepGUM is able to adapt to a continuously evolving outlier distribution, avoiding to manually impose any threshold on the proportion of outliers in the training set. Extensive experimental evaluations on four different tasks (facial and fashion landmark detection, age and head pose estimation) lead us to conclude that our novel robust technique provides reliability in the presence of various types of noise and protection against a high percentage of outliers