Among all the 3D optical flow diagnostic techniques, digital inline holographic particle tracking velocimetry (DIH-PTV) provides the highest spatial resolution with low cost, simple and compact optical setups. Despite these advantages, DIH-PTV suffers from major limitations including poor longitudinal resolution, human intervention (i.e. requirement for manually determined tuning parameters during tracer field reconstruction and extraction), limited tracer concentration, and expensive computations. These limitations prevent this technique from being widely used for high resolution 3D flow measurements. In this study, we present a novel holographic particle extraction method with the goal of overcoming all the major limitations of DIH-PTV. The proposed method consists of multiple steps involving 3D deconvolution, automatic signal-to-noise ratio enhancement and thresholding, and inverse iterative particle extraction. The entire method is implemented using GPU-based algorithm to increase the computational speed significantly. Validated with synthetic particle holograms, the proposed method can achieve particle extraction rate above 95% with fake particles less than 3% and maximum position error below 1.6 particle diameter for holograms with particle concentration above 3000 particles/mm3. The applicability of the proposed method for DIH-PTV has been further validated using the experiment of laminar flow in a microchannel and the synthetic tracer flow fields generated using a DNS turbulent channel flow database. Such improvements will substantially enhance the implementation of DIH-PTV for 3D flow measurements and enable the potential commercialization of this technique.