Traffic sign and mile-marker detection and classification are among the important components of highway asset management systems. The significant number of these high-quantity and low-cost assets in US highways can negatively impact the quality of any manual data collection and analysis. To address these challenges, this paper presents an efficient pipeline for video-based detection and classification of traffic signs and mile-markers based on color and shape criteria. Candidate extraction is based on finding the optimum RGB thresholds which yield high detection rates (very low false-negatives) while keeping the number of false-positives in check. The connected components from a thresholded image are extracted next. We use sliding windows, Haar-like features, and the AdaBoost learning method to classify the detected assets. Experimental results with an average classification accuracy of 79.30% on actual data collected from US-460 highway show the promise of the proposed method for reducing the time and effort required for developing traffic road asset inventories.