Reliable target detection and tracking within strong clutters in outdoor infrared video sequences present great challenges. This is caused by several artificial and natural conditions, such as luminance change resulted from automatic gain adjustment in the IR camera, and other extreme granularity and uncontrolled environmental factors. In some important applications, the targets of interest include vehicles in motion and people in transit. We propose a new integrated solution to address all these issues. The system is composed of the following components: a region masking algorithm to divide a video frame into reliable and unreliable regions, a novel motion pattern recognition module, and an automatic walking-person recognition module. Such a comprehensive technique makes it possible for real-world outdoor infrared surveillance applications, where environmental interferences are uncontrolled and target motion are complex. Extensive experiments were carried out on real-world outdoor infrared videos provided by General Dynamics. In all tested sequences, the proposed algorithm had successfully detected and tracked the targets consistently throughout the life cycle of the targets, even when some targets were seriously blurred or occluded. Our results show that the proposed algorithm is practical, robust, and reliable for low-quality outdoor infrared videos.
This paper focuses on applying an interval recursive least-squares (RLS) filter to a video target tracking problem. This is to circumvent the potential limitation of a RLS filter due to its sensitivity to variations in filter parameters and disturbances to state observations. Such sensitivity can make the solutions invalid in practical problems. In particular, in the application of video target tracking using a RLS filter, inaccurate parameters in the affine model may result in noticeable deviations from true target positions sufficient to lose a target. An interval RLS filter is proposed to produce state estimation and prediction in narrow intervals. Simulations show that an interval RLS filter is robust to state and observation noise and variations in filter parameters and state observations, and it outperforms an interval Kalman filter. Using an interval RLS filter, a video target tracking algorithm is developed to estimate the target position in each frame. The proposed tracking algorithm using an interval RLS filter is robust to noise in video sequences and errors in the affine models, and outperforms that using a RLS filter. Performance evaluations using real-world video sequences are provided to demonstrate the effectiveness of the proposed algorithm.
This paper focuses on applying an interval recursive least-squares (RLS) filter to a video target tracking problem.
An RLS filter can be sensitive to variations in filter parameters and disturbance to state observations to make the
solutions impractical in practical problems. Specially, in the application of video target tracking using an RLS
filter, inaccurate parameters in the affine model may result in noticeable deviations from true target positions
to lose the target. To make results robust, each filter parameter and state observation is allowed to vary in an
interval. Motivated by this idea, an interval RLS filter is proposed to produce state estimation and prediction
by narrow intervals. Simulations show that an interval RLS filter is robust to state and observation noise and
variations in filter parameters and state observations, and outperforms an interval Kalman filter. Using an
interval RLS filter, a video target tracking algorithm is developed to estimate the target position in each frame.
The proposed tracking algorithm using an interval RLS filter is robust to noise in video sequences and error of
the affine models, and outperforms that using an RLS filter. Performance evaluations using real-world video
sequences are provided to demonstrate effectiveness of the proposed algorithm.
This paper addresses two problems commonly associated with video target tracking system. First, video target
detection and tracking usually require extensive searching in a large space to find the best matches for preregistered
templates. Existing fast search methods cannot guarantee a global optimal match, which results in
substandard performance. To obtain a true global match, a full search at the pixel or sub-pixel level is required.
Obviously, this introduces significant computational overhead, which limits the implementation of these algorithms
in real-time applications. In this paper, we propose a fast method to compute two-dimensional normalized
cross-correlations to efficiently find the global optimal match result from a large image area. Comparisons and
complexity analysis are provided to show the efficiency of the proposed algorithm. Second, another challenge
commonly faced by detection and tracking systems is the accurate detection of target orientation in a twodimensional
image. This problem is motivated by applications where the walk-in and walk-out people need to
be detected and a fast image registration method is needed to compensate the change in rotation, translation
and size, which is natural since the target's distance from the camera is changing dramatically. To address this
issue, we propose a novel and efficient eigenvector-based method to detect target orientation and apply it into
automatic human recognition system. Experimental and real-world test results verify that the proposed fast
algorithm achieves similar accuracy as the recursive registration method which is computationally expensive.
This paper addresses the issue of tracking partially occluded targets in videos recorded by moving cameras of
either handhold or airborne. We propose a fast geometric constraint global motion algorithm to reduce the
computation overhead dramatically and the effect caused by outliers from moving targets. A recursive least-squares
filter with forgetting factor is utilized to filter out disturbances and to provide a better estimation of
the target's position in the current frame as well as the prediction of the position and velocity for the next
frame. The filter uses the affine model and the primary search result to construct a kinetic model. After that,
a compact search region is formed based on the prediction to reduce mismatch and improve computation speed.
The adaptive template matching is applied to improve the performance further. With these important steps, a
tracking algorithm is developed and tested on real video sequences.
This paper presents a system that creates and navigates an unlimited-size mosaic with geographical information. The input is a sequence of airborne images with or without telemetry data, and the output is a mosaic with a combined geographical coordinate layer inherited from the input images. Rather than registering input images with an orthoimage, which is popular in existing applications, the proposed system only takes use of telemetry data as prior information. The airborne images embedded with geo-information are pair-wise registered, based on image feature correspondence. We extract feature points and form a modified EDGE-based descriptor for image registration. Subsequently, the geographical coordinate layers derived from the telemetry data stream are fused using a registration matrix computed from the previous step. However, due to the unreliability of the telemetry data, the new geodetic coordinate layer might be inconsistent with the image coordinate layer and therefore requires rectification to minimize the squared error between the mosaic coordinate layer and the warped geographical coordinate layer. The above process is incorporated into a cluster framework so that the output mosaic is extensible to an infinite size. That is, once the current mosaic size has expanded beyond computer memory limitations, the image is saved to a database. Its spatial relationship with respect to the world coordinate system is also saved to the database so that the system can navigate the collection of image mosaic data by querying the spatial database and retrieving the relevant mosaics. This method is especially suitable for video sequences spanning large regions, such as surveillance video from a micro UAV. Results with real-world UAV video are provided to demonstrate the performance of the proposed system.
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