IEEE Trans Image Process - Activity based matching in distributed camera networks.

Tópicos

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Resumo

In this paper, we consider the problem of finding correspondences between distributed cameras that have partially overlapping field of views. When multiple cameras with adaptable orientations and zooms are deployed, as in many wide area surveillance applications, identifying correspondence between different activities becomes a fundamental issue. We propose a correspondence method based upon activity features that, unlike photometric features, have certain geometry independence properties. The proposed method is robust to pose, illumination and geometric effects, unsupervised (does not require any calibration objects). In addition, these features are amenable to low communication bandwidth and distributed network applications. We present quantitative and qualitative results with synthetic and real life examples, and compare the proposed method with scale invariant feature transform (SIFT) based method. We show that our method significantly outperforms the SIFT method when cameras have significantly different orientations. We then describe extensions of our method in a number of directions including topology reconstruction, camera calibration, and distributed anomaly detection.

Resumo Limpo

paper consid problem find correspond distribut camera partial overlap field view multipl camera adapt orient zoom deploy mani wide area surveil applic identifi correspond differ activ becom fundament issu propos correspond method base upon activ featur unlik photometr featur certain geometri independ properti propos method robust pose illumin geometr effect unsupervis requir calibr object addit featur amen low communic bandwidth distribut network applic present quantit qualit result synthet real life exampl compar propos method scale invari featur transform sift base method show method signific outperform sift method camera signific differ orient describ extens method number direct includ topolog reconstruct camera calibr distribut anomali detect

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