###### Abstract
The main application is based on non-visible light for arc detection [10; 11; 12], and research using visible light data mostly focuses on the acquisition of online video sequences, combined with manual observation or offline processing. The online feature localization method under the vehicle platform is affected by image complexity, which leads to key problems given the robustness required by the online operation of PCS contact analysis, and has attracted research attention. [PERSON] et al. [13] proposed a closed-loop method to analyze the contact status through s-transform according to the curve in the contact model. However, the analysis can only realize the contact judgment on the pantograph, and cannot judge and analyze the actual operation of PCS. [PERSON] et al. [14] proposed a pantograph slide detection framework based on a deep learning network, which could accurately judge the type of pantograph slide fault but could only detect the wear of the pantograph at fixed places; it is unable to carry out the online analysis of the contact state. [PERSON] et al. [15] developed tracking by mean-shift and, combined with the foreground detection, it can be as an online detection method; however, its performance was insufficient in real-time. [PERSON] et al. [16] used the kernel tracking method to achieve real-time contact wire tracking, but the detection of actual contact failures did not meet requirements. [PERSON] et al. [17] combined the threshold segmentation and line extraction method with the tracking method and proposed a contact points localization method. However, this 2D-based analysis has problems accurately determining a fault without precision measurements. [PERSON] et al. [18] proposed an accurate positioning method for contact points based on SVM classification under vehicle tilt conditions. This method has good performance in the face of illumination and rotation change but does not meet the actual online positioning requirements. [PERSON] et al. proposed an interesting scheme which corrects the image tilt of pantograph and uses the gradient and SIFT features to locate the upper surface of the pantograph slide plate by an SVM classifier. However, there was no reasonable solution for the contact state of PCS using this method. [PERSON] et al. [19] proposed an online contact point trajectory tracking method based on the 3D trajectory to analyze the PCS contact state, achieving real-time high-precision measurement and analysis, which effectively solves the current problem in the fault detection of PCS. However, the proposed algorithm does not show reliable spatial accuracy, and during long-term tracking the performance under changing illumination may not meet practical requirements. In this paper, we focus on the difficulties of online pantograph catenary image feature localization and propose an online intelligent perception of the PCS state based on parameter adaption. This paper mainly deals with the DSA-200 pantograph, which is the mainstream in China Railway Highspeed (CRH). We expect to realize the evaluation of the PCS state by analysis of the trajectory of contact points and pantograph slides, and online detection of arcing and pantograph catenary separation, which are the common PCS operation faults. Therefore, it is necessary to accurately locate the contact points, pantograph horns, and other characteristic points and robustly obtain them for a long time along with the train operation. For pantograph and catenary contact points, an adaptive tracking positioning method is proposed. We propose the addition of a result discriminator to the closed-loop tracking method so that the corresponding parameter adjustment can be carried out independently through positioning result feedback. This ensures that tracking and positioning can be achieved under a variety of light conditions under long-term train operation.