改进星型级联可形变部件模型的行人检测

目的 行人检测是计算机视觉和模式识别领域的研究热点与难点，由于经典的可形变部件模型(DPM)检测速度太慢，引入PCA降维的星型级联检测可形变部件模型(casDPM)相比较于DPM模型检测速度虽然有了很大提升，但在应用于行人检测时，出现检测精度较低、平均对数漏检率较高的情况，为了更加准确地对行人进行检测，提出了一种改进casDPM模型的行人检测方法。方法 首先利用对象度量方法获取目标候选区域，结合目标得分信息得到casDPM模型低分检测区域的置信度，在设定的阈值上保留检测窗口；然后针对casDPM模型原有非极大值抑制(Nms)算法只利用单一的面积信息，造成误检数较高的情况，提出了利用检测窗口的得分信息进行改进；最后将两种方法结合起来，提出了融合的cas-WNms-BING模型。结果 采用本文方法在INRIA数据集上进行检测，实验结果表明该方法对于行人形变、背景特征复杂及遮挡现象具有较强的鲁棒性，相比casDPM模型，本文提出的方法平均精度(AP)可以提高1.74%，平均对数漏检率可以降低4.45%。结论 提出一种改进星型级联可形变部件模型，取得一定的研究成果，在复杂的背景下，能够有效地进行行人检测，主观视觉感受和客观实验评价指标都表明该方法可以有效提升模型行人检测效果。但是，星型级联可形变部件模型训练及检测效率仍有待提高，需进一步对模型存在的一些局限性进行深入研究。

Improved pedestrian detection based on modified star-cascade DPM model

Objective Pedestrian detection is a crucial research topic in computer vision and pattern recognition. Detection flows include preprocessing, feature extraction, training classification, and detection. Various human detection algorithms, which can be categorized as template matching and machine learning, have been developed in the past decades. Machine learning-based algorithms are the primary pedestrian detection method. The speed of machine learning, however, is problematic. Given the low detection speed of the classic DPM model, the current study focuses on star-casCade DPM (casDPM), which integrates PCA technology. The detection speed of casDPM is significantly higher than that of the classic DPM model. However, casDPM has a lower detection precision and higher log-average miss rate (LAMR)in pedestrian detection. Therefore, we proposed an improved pedestrian-detection approach based on the casDPM model to accurately detect pedestrians. Method Objectness proposals can be classified into grouping or window scoring methods. To produce a small set of candidate object windows, we utilized a binarized normed gradient method that trains a generic objectness measure. The set of generated features is called BING. Non-maximum suppression (NMS)is an important post-processing step. The common NMS is based on a greedy strategy that only utilizes area information and disregards the detection score generated by the model. Therefore, the following strategies are employed to address these problems:first, to obtain the confidence of regions with a low detection score in the casDPM model, object score is combined with candidate object area information, which is determined by the objectness measure. Windows with a confidence level above a given threshold are retained, which helps reduce negative windows. The score of detection windows is used to modify the original NMS algorithm, which only utilizes single area information in the casDPM model to reduce the high false-positive rate. We proposed a confluent cas-WNms-BING model that integrates the two methods to fully utilize the detection of window scores and candidate object proposed by objectness measure. Result We conducted tests to evaluate the performance of the proposed algorithm. Experiments on the INRIA dataset were conducted, and results were compared with those of the casDPM model. Results indicated that the average precision of the proposed model increased by 1.74%, the LAMR decreased by 4.45%, and speed increased by more than five-fold. These results indicated that the proposed algorithm is effective and has practical applications. Conclusion Results showed that the proposed algorithm is applicable in actual pedestrian detection. The algorithm is robust against human deformation, complex background features, and occlusion. The algorithm also decreases LAMR and improves detection precision.