Video-based person reidentification is a challenging and important task in surveillance-based applications. Toward this, several shallow and deep networks have been proposed. However, the performance of existing shallow networks does not generalize well on large datasets. To improve the generalization ability, we propose a shallow end-to-end network which incorporates two stream convolutional neural networks, discriminative visual attention and recurrent neural network with triplet and softmax loss to learn the spatiotemporal fusion features. To effectively use both spatial and temporal information, we apply spatial, temporal, and spatiotemporal pooling. In addition, we contribute a large dataset of airborne videos for person reidentification, named DJI01. It includes various challenging conditions, such as occlusion, illuminationchanges, people with similar clothes, and the same people on different days. We perform elaborate qualitative and quantitative analyses to demonstrate the robust performance of the proposed model.
Extraction of elliptic shapes in real images is very challenging because the geometric shapes corresponding to the
various objects often appear incomplete and deformed due to the presence of noise, cluttered background and occlusion
by other objects. This paper proposes a new method of ellipse detection, which is able to deal with the challenges
mentioned above, while being computationally efficient and more accurate than existing methods. The novelty of the
current work is a grouping scheme based on a 'trust score' that indicates the trust that can be put upon an edge in a
group. In the first stage, partial Hough transform is performed in order to generate the possible centers (or center bins in
2-dimensional pixel space). Then, a special histogram is generated using the 'trust score' that rates the relationship of the
edge and the center bin. This histogram is used to group the edges and rank them within each group. In the second stage,
least square technique is applied in order to judge and improve the grouping and finally find the parameters of the
ellipses. Such hybrid method has various advantages like consideration of large number of possible groups,
computational efficiency, parallelizability, real time application, etc. The method performs well for complicated real
images and is suitable for real-time applications of machine vision.
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