Shadows of moving objects may cause serious problems in many computer vision applications, including object tracking and object recognition. In common object detection systems, due to having similar characteristics, shadows can be easily misclassified as either part of moving objects or independent moving objects. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. This paper addresses the main problematic situations associated with shadows and provides a comprehensive performance comparison on up-todate methods that have been proposed to tackle these problems. The evaluation is carried out using benchmark datasets that have been selected and modified to suit the purpose. This survey suggests the ways of selecting shadow detection methods under different scenarios.

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In many traffic-related applications, such as traffic management and structural health monitoring for roads, an accurate estimation of a moving vehicle's size and shape is needed before proceeding further. However, due to the presence of cast shadows, these properties cannot be obtained accurately using common object detection systems. To deal with the problem of misclassifying shadows as foreground, various methods have been introduced. Most of these methods often fail to distinguish shadow points from the foreground object when the boundary between the umbra and the object is unclear due to camouflage. A novel method for detecting moving shadows of vehicles in real-time applications is presented. The method is based on two measurements, namely, the illumination direction and the intensity measurements in the neighbouring pixels in a scanned line. A major advantage of using image lines for classification is the ability to solve the problem associated with camouflages. Experimental results show that the proposed method is efficient in real-time performances and has achieved higher detection rate and discrimination rate when compared with two well-known methods.

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For the carrier frequency offset (CFO) and sampling (clock) frequency offset (SFO) estimation, the hybrid Cramer-Rao bound (HCRB) is developed when the CFO, SFO, information-bearing symbols are deterministic and channel coefficients are random. Both noise and channel coefficients are complex Gaussian. The HCRB is a lower bound on the mean squared estimation error for any unbiased estimator of a parameter. For the HCRB to be applicable, it is necessary for deterministic parameters to be identifiable (uniquely determined). Some necessary identifiability conditions of some deterministic parameters are found and presented. The HCRB is dependent on the initial time instant. The HCRB is used to assess the performances of some existing methods via simulation. Our results demonstrate that even the best performance is still around 10 dB higher than the HCRB. Further effort is needed to develop more accurate methods.