An adaptive shadow detection algorithm is proposed to eliminate interference on object detection from the shadow. The algorithm uses three components in YUV colour space to identify shadow pixels from the candidate foreground. An adaptive threshold estimator is designed to improve shadow detection accuracy and adaptive capacity in various lighting conditions. This estimator uses edge detection method to obtain global texture, as well statistical calculations to obtain the thresholds. Algorithm has the characteristic of low complexity and little restraint; hence it is suitable for real time-moving shadow detection in various lighting conditions. Experiment results show that this algorithm can obtain a high detection accuracy and the time-assume is greatly shortened compared with other algorithms with similar accuracy.

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Pan–tilt–zoom (PTZ) cameras play an important role in visual surveillance system. Dual‐PTZ camera system is the simplest and most typical one. The superiority of this system lies in that it can obtain both large‐view information and high‐resolution local‐view information of the tracked object at the same time. One method to achieve such task is to use master–slave configuration. One camera (master) tracks moving objects at low resolution and provides the positional information to another camera (slave). Then the slave camera can point towards the object at high resolution and track it dynamically. In this paper, we propose a novel framework exploiting planar ground assumption to achieve cooperative tracking. The approach differs from conventional methods in that we exploit planar geometric constraint to solve the camera collaboration problem. Compared with the existing approach, the proposed framework can be used in the case of wide baseline, and allows the depth change of the tracked object. The proposed method can also adapt to the dynamic change of the surveillance scene. Besides, we also describe a self‐calibration method of homography matrix which is induced by the ground plane between two cameras. We demonstrate the effectiveness of the proposed method by testing it with a tracking system for surveillance applications.

Single-image dehazing is an important problem because it is a key prerequisite for most high-level computer vision tasks. Traditional prior-based methods adopt priors generated from clear images to restrain the atmospheric scattering model and then recover haze-free images. However, these prior-based methods always encounter over-enhancement, such as halos and colour distortion. To solve this problem, many works use a convolutional neural network to retrieve original images. However, without priors as guidance, these learning-based methods dehaze effectively in synthetic datasets but perform poorly in real scenes. Hence, in this paper, we propose a prior-guided multiscale network for singleimage dehazing named PGMNet. Specifically, prior-based methods are adopted to acquire dehazed images of the training dataset in advance and then send these dehazed images to a parameter-shared encoder to form multiscale features. During the decoding process, these multiscale features are adopted to guide the prior-guided multiscale network to recover more image details. Moreover, considering that these prior-based dehazed images usually contain some over-enhanced regions, a spatial attention guided feature aggregation module and squeeze-and-excitation module are adopted to alleviate colour distortion. The proposed PGMNet takes the advantage of prior-based methods in real haze removal and provides superior performance compared with the state-of-the-art methods on both synthetic and real-world datasets.