In this work, we present a comparative evaluation of various 'tracking-by-detection' approaches on public datasets. The work investigates popular sequential Monte Carlo and template ensemble based trackers coupled with relevant visual people detectors with emphasis on exhibited performance variation depending on tracker-detector choice. Extensive experimental results are provided on public dataset and results indicate the choice of a detector can significantly vary the performance of a tracker. Our experimental results show, depending on the choice of the detector, average tracking accuracy across three public datasets could exhibit a 45% standard deviation with only, on average, a 6.8% and 11.1% standard deviation in detector recall and precision respectively.