The use of passive infrared (PIR) triggered camera traps has dramatically increased in recent decades. Unfortunately, technical descriptions of how PIR triggered camera traps operate have not been sufficiently clear. Descriptions have often been ambiguous or misleading and in several cases are demonstrably wrong. Such descriptions have led to erroneous interpretations of camera trapping data. This short communication clarifies how PIR sensors operate. We clarify how infrared radiation is emitted and transmitted, and we describe the parts of the PIR sensor and how they detect infrared radiation and, by extension, fauna. Several problematic descriptions of PIR sensors are drawn on to highlight flawed descriptions and demonstrate where erroneous interpretations of camera trapping data occurred. By clarifying the language and the description of PIR triggered camera traps, this paper ensures that wildlife researchers and managers using camera traps will avoid flawed interpretations of their data. Avoiding flawed interpretations of data should reduce wasted effort and resources that would otherwise come about as researchers attempt to test flawed hypotheses. Furthermore, this paper provides a thorough technical reference for camera trapping practitioners, which is not present elsewhere in the wildlife research literature.

Illegal distribution of a digital movie is a significant threat to the film industries. With the advent of high-speed broadband Internet access, a pirated copy of a digital video can be easily distributed to a global audience. Digital video watermarking is a possible means of limiting this type of digital distribution. In existing watermarking methods, the watermark is usually embedded into the luminance channel of a video frame, which affects imperceptibility. In addition, none of the existing techniques are robust to the combination of commonly used attacks, such as compression, upscaling, rotation, cropping, downscaling in resolution, frame rate conversion, and camcording. In this paper, we initially propose a basic blind digital video watermarking algorithm, where the watermark is embedded into one level of the dual-tree complex wavelet transform of the chrominance channel to provide high quality watermarked video and extracted using the same key that was used for embedding. This algorithm is robust to compression, upscaling, rotation, and cropping. An extension of this method extracts the watermark from any level(s) of the dual-tree complex wavelet transform depending on the resolution of the downscaled version of the watermarked frame rather than only from the embedding level to survive downscaling to an arbitrary resolution. Finally, the watermark of a frame is extracted from the information of that frame without using the key that was used during watermark embedding to provide robustness to temporal synchronization attacks, such as frame rate conversion. This scheme is also robust to compression, camcording, watermark estimation remodulation, temporal frame averaging, multiple watermark embedding, downscaling in resolution, and other geometric attacks, such as upscaling, rotation, and cropping.

The popularity of 3D video is increasing daily due to the availability of low-cost 3D televisions and high-speed Internet access. However, currently the contents of 3D video can be distributed illegally without any protection. For views generated using a depth-image-based rendering technique, not only the left and right views can be distributed as 3D content, but also the center, left, or right views individually as 2D content. As digital video watermarking is a possible way of protecting these views from unauthorized distribution, in this paper, we propose a digital watermarking method for depth-image-based rendered 3D video. In this method, the watermark is embedded in both of the chrominance channels of a YUV representation of the center view using the dual-tree complex wavelet transform. Then, the left and right views are generated from the watermarked center view and depth map using a depth-image based rendering technique. Finally, the watermark can be extracted from the center, left, and right views in a blind fashion without using the original unwatermarked center, left, or right views. This watermark is robust to geometric distortions, such as upscaling, rotation and cropping, downscaling to an arbitrary resolution, and the most common video distortions, including lossy compression and additive noise. Due to the approximate shift invariance characteristic of the dual-tree complex wavelet transform, the technique is robust against distortions in the left and right views generated using depth-image based rendering. The proposed method can also survive baseline distance adjustment and both 2D and 3D camcording.

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We propose a new algorithm to recover a geometrically correct image of an object or scene from a set of images distorted by the wave motion of a water surface. Under mild conditions where the wavy surface normals weakly satisfy a Gaussian distribution, we demonstrate that the geometric distortion can be removed and a corrected image can be recovered. Our method is based on higher-order spectra analysis—in particular, the bispectrum, similar to its use in astronomical speckle imaging. In adapting this technique to imaging through or over a moving water surface, special care must be taken, and specifically tailored techniques are discussed in this paper. Our algorithm has been tested under two different scenarios: the refraction of light through a water surface (the underwater case) and the reflection of light from a water surface (the reflection case). Results in both cases have been encouraging.