The development of the lighting profession toward the third stage requires our attention shifting from the light on certain planes to the light distributions in 3D spaces. In this article, we propose a practical strategy to measure the local density of illumination in 3D scenes based on the zeroth-order of spherical harmonics decompositions of a high dynamic range (HDR) panoramic map. The basic functional principle of deriving illuminance density from HDR panoramic maps was presented, and hereinafter named as illuminance panoramic. Illuminance panoramic was compared with Cuttle’s approximated illuminance scalar, which is essentially a physical approximation of the average illumination over a sphere. To verify the measurements, the average illuminance over a sphere, approximated illuminance scalar, illuminance panoramic, cylindrical illuminance, semi-cylindrical illuminance, and horizontal illuminance were simulated via a model (probe in a sphere). The results indicate that the measurement of density of illumination using HDR panoramic maps has well coincided with its definition (i.e., the average illuminance over a sphere) while other illumination values vary with how the probes are located. The measurement theories were later verified using six HDR panoramic maps of real scenes. This research provides confidence in developing applications in mobile phones by capturing HDR panoramic maps to measure the density of illumination in 3D spaces.
To improve the tracking accuracy of persons in the surveillance video, we proposed an algorithm for multi-target tracking persons based on deep learning. In this paper, we used You Only Look Once v5 (YOLOv5) to obtain person targets of each frame in the video and used Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) to do cascade matching and Intersection Over Union (IOU) matching of person targets between different frames. To solve the IDSwitch problem caused by the low feature extraction ability of the Re-Identification (ReID) network in the process of cascade matching, we introduced Spatial Relation-aware Global Attention (RGA-S) and Channel Relation-aware Global Attention (RGA-C) attention mechanisms into the network structure. The pre-training weights are loaded for Transfer Learning training on the dataset CUHK03. To enhance the discrimination performance of the network, we proposed a new loss function design method, which introduces the Hard-Negative-Mining way into the benchmark triplet loss.To improve the classification accuracy of the network, we introduced a Label-Smoothing regularization method to the cross-entropy loss. To facilitate the model's convergence stability and convergence speed at the early training stage and to prevent the model from oscillating around the global optimum due to excessive learning rate at the later stage of training, this paper proposed a learning rate regulation method combining Linear-Warmup and exponential decay. The experimental results on CUHK03 show that the mean Average Precision (mAP) of the improved ReID network is 76.5%. The Top 1 is 42.5%, the Top 5 is 65.4%, and the Top 10 is 74.3% in Cumulative Matching Characteristics (CMC); Compared with the original algorithm, the tracking accuracy of the optimized DeepSORT tracking algorithm is improved by 2.5%, the tracking precision is improved by 3.8%. The number of identity switching is reduced by 25%. The algorithm effectively alleviates the IDSwitch problem, improves the tracking accuracy of persons, and has a high practical value.
Scalar illuminance, which describes the constant illumination from all directions, is an important indicator of the abundance of light for a lit object and the adequacy of illumination perceived. This paper proposes a more reliable method to recover scalar illuminance based on tests in natural complex lighting environments. The performance of Cuttle’s Approach 1, Mangkuto’s Approach 2 and Approach 3, together with Xia et al.’s potential Approach 4, were tested under a total of 610 high dynamic range (HDR) panoramic maps of real scenes. The relationships between predicted scalar illuminance and normalised diffuseness levels were checked. The results indicate that the potential Approach 4 is more robust to the cubic meter’s postures, and the predicted scalar illuminance has a regular relationship with normalised diffuseness levels. Approach 4 was corrected, together with Approach 1, formulating a new method named Approach 5S. Later, the proposed Approach 5S was evaluated under 205 indoor and 2233 outdoor panoramas from the Laval HDR databases, and it was shown to recover more reliable scalar illuminance with an average error within 5% in general. This study has provided a practical solution to more accurate vector illuminance-based metrics in real lighting environments. This algorithm can be further integrated into the development of cubic illumination meter instruments.
The cubic illumination metre has been taken as the most thorough and practical tool for the field measurement of vector illuminance so far. However, uncertainty in the measurement of scalar illuminance has influenced its reliability. This study determined how the errors of the existing approaches were generated using illustrations and simulations based on the illuminance solid concept. Following the error analysis of the existing approaches, potential alternative solutions for improving its accuracy were proposed. This study can help pave the way for developing more accurate vector illuminance-based metrics in natural lighting environments using a cubic metre.
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