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. 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. 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. 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.