Detecting people carrying objects detection and classification is a problem known from surveillance scenarios. It can be used as a first step in order to monitor interactions between people and objects, like depositing or removing an object. Research is focused on new machine learning approaches for pedestrian detection and new ways of feature representation, behavior analysis and machine learning techniques for classification.
The availability of dense motion information in computer vision domain allows for the effective application of Lagrangian techniques that have their origin in fluid flow analysis and dynamical systems theory. A well established technique that has been proven to be useful in image- based crowd analysis are Finite Time Lyapunov Exponents (FTLE). Based on this, we present a method to detect people carrying object and describe a methodology how to apply established flow field methods onto the problem of describing individuals. Further, we reinterpret Lagrangian features in relation to the underlying motion process and show their applicability towards the appearance modeling of pedestrians. This definition allows to increase performance of state-of-the-art methods and is shown to be robust under varying parameter settings and different optical flow extraction approaches.
Recent work relies on a precise foreground object segmentation, which is often difficult to achieve in video surveillance sequences due to a bad contrast of the foreground objects with the scene background, abrupt changing light conditions and small camera vibrations. In order to cope with these difficulties we propose an approach based on motion statistics. Therefore we use a Gaussian mixture motion model (GMMM) and, based on that model, we define a novel speed and direction independent motion descriptor in order to detect carried baggage as those regions not fitting in the motion description model of an average walking person. The system was tested with the public dataset PETS2006 and a more challenging dataset including abrupt lighting changes and bad color contrast and compared with existing systems.
Detecting people carrying objects is a commonly formulated problem which results can be used as a first step in order to monitor interactions between people and objects in computer vision applications. In this paper we propose a novel method for this task. By using gray-value information instead of the contours obtained by a segmentation process we build up a system that is robust against segmentation errors.