[Company Logo Image]  (*)

Home Up Feedback Contents Search

Target Tracking   

HeadWheelchair ClipOn EMG SmartSpeakingKeyboard Virtual Multi Stereopsis Plant Phenotype 3D using Octree Limb-Volume measurement using Infrared Depth Sensor Object_Recognition Scene Understanding Field Phenotyping Background Subtraction Human Motion Calibration of VSNs Image-Based Servoing Robot Navigation Path Planning Inverse Kinematics Target Tracking Target Geolocation ODI Virtual Dermatologist CNN Virtual Machines for Image Processing New Models for Parallel Soft Computing



Target Tracking from Airborne Video

by Kyng min Han


Reconnaissance and surveillance in urban environments is a challenging problem especially because of the usual limited visibility, the complexity of background activity, etc. These problems can be further worsened if the source of the video imagery is airborne. In that case, the motion of the camera increases the background activity and tasks such as segmentation of target objects from the background becomes even more difficult. Unfortunately, without such segmentation of potential targets, the detection, the identification, and the geo-location of these same targets would have to be carried out on the entirety of the image frame, increasing the possibility of false detections and making it even harder to be accomplished in real-time.

In this research, we developed a fast, real-time, and effective method for segmentation of multiple moving targets from moving backgrounds. The method relies on the calculation of a differential optical flow, which can separate the motion of the airborne camera from the moving targets. Once segmented out from its background, the images of possible targets are handled as independent ROIs (regions of interest). Each ROI can be streamed into the second phase of the algorithm for tracking and geo-location of specific targets.


Algorithm overview

The algorithm always requires two images: the current image and the previous image. As soon as these images are read from disk, the Phase 1 of the algorithm calculates the Optical Flow of the current image with respect to the previous one – since the algorithm runs in real time (30fps) the images can also be fed directly from a camera. In Figure 2, we depict two typical images in a video sequence.

The OF (Figure 3a) is then analyzed and two histograms are built. The histograms represent the distributions of the pixel velocities in terms of their magnitude and orientation (Figure 4). Based on these distributions, we can separate the foreground image from the background. Each foreground blob is them processed and segmented using a morphological filter and a component labeling algorithm.



(a)                                                                         (b)

Figure 2 – Original sequence: airborne camera moves towards northwest, while the gray car in the center of the image moves in the same direction.


(a)                                                                   (b)

Figure 3 – Optical Flow, before (a) and (b) after removal of background motion (but before filtering of spurious flows in the image).


   (a)                                                              (b)

Figure 4 – Histogram analysis of the Optical Flow. (a) Magnitudes and (b) Angles.

A ROI is defined around each object segmented from the background and, based on the object’s velocity and size, the most prominent ROI is streamed into the second phase of the algorithm. Figure 3b presents a group of blobs clustered by the similarity of their OF. This is the result obtain before the morphological filtering and component labeling. Figure 5a presents the output at the end of Phase 1, with one single ROI already delineated.

Single target tracking - A dominant moving target can be segmented out by the method discussed above. Notice that there is a severe background motion in the video below.

  • When a moving target is found by the phase I processing (optical flow method), it opens a ROI on the centroid of the target. Then the phase II processing tracks each object in each ROI.


Multi-target tracking using threads - The above approach can be easily extended by using multi threading. That is, each thread process each detected target. Theoretically, it is possible to run each thread in different machine, so that even multi-tracking task can be achieved in real time.



  1. Han, K. and DeSouza, G. N., " Two Phased Bayesian Filter Applied to Vision Based Geolocation of Moving Targets", Journal of Intelligent and Robotic Systems (submitted)

  2. Han, K. and DeSouza, G. N., " Target Geolocation from Airborne Video without Terrain Data: A Comprehensive Framework", Journal of Intelligent and Robotic Systems (accepted).

  3. Han, K., Dong Y. and DeSouza, G.N., " Tracking Moving Objects from Airborne Video Using Sparse SIFT Flows and Relaxation Labeling", in the Proceedings of the 2011 IEEE International Conference on Robotic System (IEEE-ICRA) (submitted).

  4. Han, K. and DeSouza, G. N., "Multiple Target Geo-location using SIFT and Multi-Stereo Vision on Airborne Video Sequences", in Proceedings of the 2009 IEEE International Conference on Robotic System (IROS), pp. 5327-5332, Oct./09.

  5. Han, K. and DeSouza, G. N., "Instantaneous Geo-Location of Multiple Targets From Monocular Airborne Video", 2009 IEEE International Geoscience & Remote Sensing Symposium (IGARSS), pp. IV 1003-6, July 2009, Cape Town, South Africa.



Home ] Up ]

Send mail to webmaster@ee.missouri.edu with questions or comments about this web site.
Last modified: 06/26/16
(*) Logo created by James Wong