Video Surveillance
The dataset consists of six .mat files containing three surveillance video test sequences, Hall_qcif_330 (Hall, 330 frames), PETS2009_S1L1-View_001 (PETS, 100 frames), and Crosswalk (CW, 270 frames), and the corresponding background image for three videos (Only the data of each video's gray channel component). Hall is shot indoors and disturbed by noise, PETS is shot outdoors with less noise, and CW is shot outdoors with heavy noise interference. Hall and PETS are two foreground-sparse videos with small objects. CW is a foreground-dense video with dramatic changes in sparsity. All the video
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We build an original dataset of thermal videos and images that simulate illegal movements around the border and in protected areas and are designed for training machines and deep learning models. The videos are recorded in areas around the forest, at night, in different weather conditions – in the clear weather, in the rain, and in the fog, and with people in different body positions (upright, hunched) and movement speeds (regu- lar walking, running) at different ranges from the camera.
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This Dataset contains "Pristine" and "Distorted" videos recorded in different places. The
distortions with which the videos were recorded are: "Focus", "Exposure" and "Focus + Exposure".
Those three with low (1), medium (2) and high (3) levels, forming a total of 10 conditions
(including Pristine videos). In addition, distorted videos were exported in three different
qualities according to the H.264 compression format used in the DIGIFORT software, which were:
High Quality (HQ, H.264 at 100%), Medium Quality (MQ, H.264 at 75%) and Low Quality
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