In this paper we present a novel measure of camera focus based on the Bayes spectral entropy of an image spectrum. In order to estimate the degree of focus, the image is divided into non-overlapping subimages of 8 by 8 pixels. Next, sharpness values are calculated separately for each sub-image and their mean is taken as a measure of the overall focus. The sub-image spectra are obtained by an 8 × 8 discrete cosine transform (DCT). Comparisons were made against four well-known measures that were chosen as reference, on images captured with a standard visible-light camera and a thermal camera. The proposed measure outperformed the reference measures by exhibiting a wider working range and a smaller failure rate. To assess its robustness to noise, additional tests were conducted with noisy images.
In this paper we present an efficient algorithm for tracking multiple players during indoor sports matches. A sports match can be considered as a semi-controlled environment for which a set of closed-world assumptions regarding the visual as well as the dynamical properties of the players and the court can be derived. These assumptions are then used in the context of particle filtering to arrive at a computationally fast, closed-world, multi-player tracker. The proposed tracker is based on multiple, single-player trackers, which are combined using a closed-world assumption about the interactions among players. With regard to the visual properties, the robustness of the tracker is achieved by deriving a novel sports-domain-specific likelihood function and employing a novel background-elimination scheme. The restrictions on the player's dynamics are enforced by employing a novel form of local smoothing. This smoothing renders the tracking more robust and reduces the computational complexity of the tracker. We evaluated the proposed closed-world, multi-player tracker on a challenging data set. In comparison with several similar trackers that did not utilize all of the closed-world assumptions, the proposed tracker produced better estimates of position and prediction as well as reducing the number of failures.
6A novel method for efficient encoding human body motion, extracted from image sequences is presented.
7Optical flow field is calculated from sequential images, and the part of the flow field containing a person is 8 subdivided into six segments. For each of the segments, a two dimensional, eight-bin histogram of optical 9 flow is calculated. A symbol is generated, corresponding to the bin with the maximum sample count.
10Since the optical flow sequences before and after the temporal reference point are processed separately, 11 twelve symbol sequences are obtained from the whole image sequence. Symbol sequences are purged of 12 all symbol repetitions. To establish the similarity between two motion sequences, two sets of symbol 13 sequences are compared. In our case, this is done by the means of normalized Levenshtein distance. Due 14 to use of symbol sequences, the method is extremely storage efficient. It is also performance efficient, as 15 it could be performed in near real-time using the motion vectors from MPEG4 encoded video sequences.
16The approach has been tested on video sequences of persons entering restricted area using keycard and 17 fingerprint reader. We show that it could be applied both to verification of person identities due to 18 minuscule differences in their motion, and to detection of unusual behavior, such as tailgating.
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