In recent years, web tracking has raised concerns about privacy during web browsing. This paper introduces SEQ-Track, a novel method for detecting web tracking behaviors by leveraging length and time information in encrypted traffic. SEQ-Track extracts the packet length sequences and time interval sequences of packets from network traffic flows and utilizes feature extractors based on the Convolutional Neural Network (CNN) and Transformer to perform web tracking detection. Our experimental results demonstrate that SEQ-Track performs well with over 90% accuracy across key evaluation metrics. The ablation study underscores the importance of the two sequences in the detection process. It also reveals that introducing statistical features results in minimal performance improvement while increasing computational complexity, highlighting the importance of careful feature selection, rather than simply increasing the number of features.