3 research outputs found
Three hypothesis algorithm with occlusion reasoning for multiple people tracking
This work proposes a detection-based tracking algorithm able to locate and keep the identity of multiple
people, who may be occluded, in uncontrolled stationary environments. Our algorithm builds a tracking graph
that models spatio-temporal relationships among attributes of interacting people to predict and resolve partial
and total occlusions. When a total occlusion occurs, the algorithm generates various hypotheses about the
location of the occluded person considering three cases: (a) the person keeps the same direction and speed,
(b) the person follows the direction and speed of the occluder, and (c) the person remains motionless during
occlusion. By analyzing the graph, our algorithm can detect trajectories produced by false alarms and estimate
the location of missing or occluded people. Our algorithm performs acceptably under complex conditions, such
as partial visibility of individuals getting inside or outside the scene, continuous interactions and occlusions
among people, wrong or missing information on the detection of persons, as well as variation of the person’s
appearance due to illumination changes and background-clutter distracters. Our algorithm was evaluated on
test sequences in the field of intelligent surveillance achieving an overall precision of 93%. Results show
that our tracking algorithm outperforms even trajectory-based state-of-the-art algorithms
An algorithm for multiple object tracking
Background for multiple object tracking -- Data association -- The model of object
Multi-Object Trajectory Tracking
The majority of existing tracking algorithms are based on the maximum a posteriori (MAP) solution of a probabilistic framework using a Hidden Markov Model, where the distribution of the object state at the current time instance is estimated based on current and previous observations. However, this approach is prone to errors caused by distractions such as occlusions, background clutters and multi-object confusions. In this paper we propose a multiple object tracking algorithm that seeks the optimal state sequence that maximizes the joint multi-object state-observation probability. We call this algorithm trajectory tracking since it estimates the state sequence or “trajectory ” instead of the current state. The algorithm is capable of tracking unknown time-varying number of multiple objects. We also introduce a novel observation model which is composed of the original image, the fore-ground mask given by background subtraction and the object detection map generated by an object detector. The image provides the object appearance information. The foreground mask enables the likelihood computation to consider the multi-object configuration in its entirety. The detection map consists of pixel-wise object detection scores, which drives the tracking algorithm to perform joint inference on both the number of objects and their configurations efficiently. The proposed algorithm has been implemented and tested extensively in a complete CCTV video surveillance system to monitor entries and detect tailgating and piggy-backing violations at access points for over six months. The system achieved 98.3 % precision in event classification. The viola-tion detection rate is 90.4 % and the detection precision is 85.2%. The results clearly demonstrate the advantages of the proposed detection based trajectory tracking framework. 1