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While training a cascade, we use more negative image samples as compared to positive images. The reason behind doing so is enabling the cascade to reject non-object region easily and thus reducing false detection and increasing computational efficiency. While training the cascade, we decide the window size. The cascade can detect objects with minimum size that of window. Also, it randomly picks samples of window size from the negative images. So you could have only 1000 unique negative images of 500500 but -numNeg could be 10000 with window size of 5050.

While training a cascade, we use more negative image samples as compared to positive images. The reason behind doing so is enabling the cascade to reject non-object region easily and thus reducing false detection and increasing computational efficiency. While training the cascade, we decide the window size. The cascade can detect objects with minimum size that of window. Also, it randomly picks samples of window size from the negative images. So you could have only 1000 unique negative images of 500500 X 500 but -numNeg could be 10000 with window size of 5050 X 50. This helps having more negative samples withoutthe need of having unique negative images. Hope this clears stuff!

While training a cascade, we use more negative image samples as compared to positive images. The reason behind doing so is enabling the cascade to reject non-object region easily and thus reducing false detection and increasing computational efficiency. While training the cascade, we decide the window size. The cascade can detect objects with minimum size that of window. Also, it randomly picks samples of window size from the negative images. So you could have only 1000 unique negative images of 500 X 500 but -numNeg could be 10000 or more with window size of 50 X 50. This helps having more negative samples withoutthe need of having unique negative images. Hope this clears stuff!