Tracking Of Individual Objects Research Articles

Counting-Based Particle Flux Estimation for Traffic Analysis in Live Cell Imaging

A quantitative analysis of the dynamic contents in fluorescence time-lapse microscopy is crucial to decipher the molecular mechanisms involved in cell functions. In this paper, we propose an original traffic analysis approach based on the counting of particles from frame to frame. The suggested method lies between individual object tracking and dense motion estimation (i.e., optical flow). Instead of tracking each moving particle, we estimate fluxes of particles between predefined and adjacent regions. The problem is formulated as the minimization of a global cost function and the approach allows us to process image sequences with a high number of particles and a high rate of particle appearances and disappearances. We propose to study the influence of object density, image partition scale, motion amplitude, and particle appearances/disappearances in a large variety of simulations. The potential of the method is finally demonstrated on real image sequences showing GFP-tagged Rab6 trafficking in confocal microscopy.

A collaborative scheme for boundary detection and tracking of continuous objects in WSNs

With rapid advancements in MEMS technologies, sensor networks have made possible a broad range of applications in real time. Object tracking and detection is one of the most prominent applications for wireless sensor networks. Individual object tracking and detection has been intensively discussed, such as tracking enemy vehicles and detecting illegal border crossings. The tracking and detection of continuous objects such as fire smoke, nuclear explosions and hazardous bio-chemical material diffusions pose new challenges because of the characteristics of such objects, i.e., expanding and shrinking in size, changing in shape, splitting into multiple objects or merging multiple objects into one over time. A continuous object covers a large area over the network and requires extensive communication to detect and track. Tracking continuous objects accurately and efficiently is challenging, whereas extensive communication consumes massive energy in the network, and handling energy in wireless sensor network (W...

Detection of dominant flow and abnormal events in surveillance video

We propose an algorithm for abnormal event detection in surveillance video. The proposed algorithm is based on a semi-unsupervised learning method, a kind of feature-based approach so that it does not detect the moving object individually. The proposed algorithm identifies dominant flow without individual object tracking using a latent Dirichlet allocation model in crowded environments. It can also automatically detect and localize an abnormally moving object in real-life video. The performance tests are taken with several real-life databases, and their results show that the proposed algorithm can efficiently detect abnormally moving objects in real time. The proposed algorithm can be applied to any situation in which abnormal directions or abnormal speeds are detected regardless of direction.

Tracking of Multiple Targets Using Online Learning for Reference Model Adaptation

Recently, much work has been done in multiple object tracking on the one hand and on reference model adaptation for a single-object tracker on the other side. In this paper, we do both tracking of multiple objects (faces of people) in a meeting scenario and online learning to incrementally update the models of the tracked objects to account for appearance changes during tracking. Additionally, we automatically initialize and terminate tracking of individual objects based on low-level features, i.e., face color, face size, and object movement. Many methods unlike our approach assume that the target region has been initialized by hand in the first frame. For tracking, a particle filter is incorporated to propagate sample distributions over time. We discuss the close relationship between our implemented tracker based on particle filters and genetic algorithms. Numerous experiments on meeting data demonstrate the capabilities of our tracking approach. Additionally, we provide an empirical verification of the reference model learning during tracking of indoor and outdoor scenes which supports a more robust tracking. Therefore, we report the average of the standard deviation of the trajectories over numerous tracking runs depending on the learning rate.